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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 sources NASA NTRS DOE OSTI

NASA NTRS1997

The Web Measurement Environment (WebME): A Tool for Combining and Modeling Distributed Data

Many organizations have incorporated data collection into their software processes for the purpose of process improvement. However, in order to improve, interpreting the data is just as important as the collection of data. With the increased presence of the Internet and the ubiquity of the World Wide Web, the potential for software processes being distributed among several physically separated locations has also grown. Because project data may be stored in multiple locations and in differing formats, obtaining and interpreting data from this type of environment becomes even more complicated. The Web Measurement Environment (WebME), a Web-based data visualization tool, is being developed to facilitate the understanding of collected data in a distributed environment. The WebME system will permit the analysis of development data in distributed, heterogeneous environments. This paper provides an overview of the system and its capabilities.

Tesoriero, Roseanne↗

DOE OSTI2022

Improving future travel demand projections: a pathway with an open science interdisciplinary approach

Transport accounts for 24% of global CO 2 emissions from fossil fuels. Governments face challenges in developing feasible and equitable mitigation strategies to reduce energy consumption and manage the transition to low-carbon transport systems. To meet the local and global transport emission reduction targets, policymakers need more realistic/sophisticated future projections of transport demand to better understand the speed and depth of the actions required to mitigate greenhouse gas emissions. In this paper, we argue that the lack of access to high-quality data on the current and historical travel demand and interdisciplinary research hinders transport planning and sustainable transitions toward low-carbon transport futures. We call for a greater interdisciplinary collaboration agenda across open data, data science, behaviour modelling, and policy analysis. These advancemets can reduce some of the major uncertainties and contribute to evidence-based solutions toward improving the sustainability performance of future transport systems. The paper also points to some needed efforts and directions to provide robust insights to policymakers. We provide examples of how these efforts could benefit from the International Transport Energy Modeling Open Data project and open science interdisciplinary collaborations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2004

IGS Data Center Working Group Report

At its 18th meeting held December 09, 2001 in San Francisco, the IGS Governing Board recommended the formation of a working group to focus on data center issues. This working group will tackle many of the problems facing the IGS data centers as well as develop new ideas to aid users both internal and external to the IGS. The direction of the IGS has changed since its start in 1992 and many new working groups, projects, data sets, and products have been created and incorporated into the service since that time. Therefore, this may be an appropriate time to revisit the requirements of data centers within the IGS.

Noll, Carey E.↗

DOE OSTI2025

X-ray Computed Tomography Data of Dense Metallic Components

The data shared in here are X-ray computed tomography (XCT) scans of a hexagonal fuel nozzle in 3 sections with the Metrotom 800 system at the Manufacturing Demonstration Facility (MDF) at Oak Ridge National Laboratory. The data are used in the paper "Tomographic Sparse View Selection using the View Covariance Loss, by Lin et al. (doi:10.1109/TPAMI.2025.36000720), accepted to the international conference on computational imaging (ICCP 2025). Figures 4-7 in the paper describe the part/XCT scan. File name Descriptions: Bottom section: TCR- Single Channeled SRC L 2019-3-18 12-26-41.hdf5 Medium section: TCR- Single Channeled SRC M 2019-3-18 13-8-9.hdf5 Top section: TCR- Single Channeled SRC T 2019-3-18 13-45-39.hdf5 Each hdf5 file contains projection data, and all the relevant X-ray CT scan setting. The full list of included attributes: distance_unit: Units of all distances specified angle_unit : Units of the angles angles: Array of all angles used voxel_size_xy: Baseline recon (if any) has this voxel size in the in-plane direction voxel_size_z: Baseline recon (if any) has this voxel size in the cross-plane direction det_pixel_size_col: Size of the detector pixels in the column dimension det_pixel_size_row: Size of the detector pixels in the row dimension src_iso_dist: Source to iso-center distance iso_det_dist: Iso-center to detector distance det_angle: If the detector is rotated/tilted, this angle corresponds to that value det_row_offset: Center of rotation offset in the vertical direction det_col_offset: Center of rotation offset in the horizontal direction reconstruction: A baseline reconstruction stored as 3D array BHC params: Beam-hardening parameters - Van De Casteel Model - if it has been used to pre-process the projections We also provided a python script (hdf_io.py) that allows the user to read the relevant data from each hdf5 file.

Ziabari, Amir [Oak Ridge National Laboratory]↗

DOE OSTI2021

State and Local Planning for Energy (SLOPE) Platform

This fact sheet outlines the basic overview features, purpose, and additional information for the State and Local Planning for Energy (SLOPE) Platform. SLOPE is a collaboration between nine U.S. Department of Energy (DOE) technology offices and the National Renewable Energy Laboratory (NREL), integrates and delivers jurisdictionally resolved potential and projection data on energy efficiency, renewable energy, and sustainable transportation in an easy-to-access online platform to enable data-driven state and local energy planning and decision-making.

48 EE - Weatherization and Intergovernmental Progr↗

NASA NTRS2000

A New Neural Network Approach Including First-Guess for Retrieval of Atmospheric Water Vapor, Cloud Liquid Water Path, Surface Temperature and Emissivities Over Land From Satellite Microwave Observations

The analysis of microwave observations over land to determine atmospheric and surface parameters is still limited due to the complexity of the inverse problem. Neural network techniques have already proved successful as the basis of efficient retrieval methods for non-linear cases, however, first-guess estimates, which are used in variational methods to avoid problems of solution non-uniqueness or other forms of solution irregularity, have up to now not been used with neural network methods. In this study, a neural network approach is developed that uses a first-guess. Conceptual bridges are established between the neural network and variational methods. The new neural method retrieves the surface skin temperature, the integrated water vapor content, the cloud liquid water path and the microwave surface emissivities between 19 and 85 GHz over land from SSM/I observations. The retrieval, in parallel, of all these quantities improves the results for consistency reasons. A data base to train the neural network is calculated with a radiative transfer model and a a global collection of coincident surface and atmospheric parameters extracted from the National Center for Environmental Prediction reanalysis, from the International Satellite Cloud Climatology Project data and from microwave emissivity atlases previously calculated. The results of the neural network inversion are very encouraging. The r.m.s. error of the surface temperature retrieval over the globe is 1.3 K in clear sky conditions and 1.6 K in cloudy scenes. Water vapor is retrieved with a r.m.s. error of 3.8 kg/sq m in clear conditions and 4.9 kg/sq m in cloudy situations. The r.m.s. error in cloud liquid water path is 0.08 kg/sq m . The surface emissivities are retrieved with an accuracy of better than 0.008 in clear conditions and 0.010 in cloudy conditions. Microwave land surface temperature retrieval presents a very attractive complement to the infrared estimates in cloudy areas: time record of land surface temperature will be produced.

Aires, F.↗

NASA NTRS1993

Optimal resolution in maximum entropy image reconstruction from projections with multigrid acceleration

We consider the problem of image reconstruction from a finite number of projections over the space L(sup 1)(Omega), where Omega is a compact subset of the set of Real numbers (exp 2). We prove that, given a discretization of the projection space, the function that generates the correct projection data and maximizes the Boltzmann-Shannon entropy is piecewise constant on a certain discretization of Omega, which we call the 'optimal grid'. It is on this grid that one obtains the maximum resolution given the problem setup. The size of this grid grows very quickly as the number of projections and number of cells per projection grow, indicating fast computational methods are essential to make its use feasible. We use a Fenchel duality formulation of the problem to keep the number of variables small while still using the optimal discretization, and propose a multilevel scheme to improve convergence of a simple cyclic maximization scheme applied to the dual problem.

Limber, Mark A.↗

DOE OSTI2023

PCAfold 2.0—Novel tools and algorithms for low-dimensional manifold assessment and optimization

We describe an update to our open-source Python package, PCAfold, designed to help researchers generate, analyze and improve low-dimensional data manifolds. In the current version, PCAfold 2.0, we introduce novel tools and algorithms for assessing and optimizing low-dimensional manifolds. This includes a method that generates a “map” of local feature sizes that can help pinpoint researchers to problematic regions on a manifold. We introduce a novel cost function that characterizes the quality of a manifold topology with a single number. We develop two algorithms for feature selection based on principal component analysis (PCA) that use the cost function as an objective function to minimize. We introduce a quantity of interest (QoI)-aware dimensionality reduction strategy where data projections are computed using an artificial neural network and are directly optimized towards representing various projection-independent and projection-dependent QoIs. We also introduce an implementation of partition of unity networks (POUnets) for efficient reconstruction of QoIs from low-dimensional manifolds based on combining neural network classification with localized polynomial regression. Our software can be broadly applicable in all domains of science and engineering that aim to reduce data dimensionality, as well as in the fundamental research on representation learning.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Sister Rod Destructive Examinations (FY22) Appendix F2: Evaluation of Fuel Rod Fatigue During Spent Fuel Transportation

This report documents work performed under the Spent Fuel and Waste Disposition’s Spent Fuel and Waste Science and Technology program for the US Department of Energy (DOE) Office of Nuclear Energy (NE). This work was performed to fulfill Level 2 Milestone M2SF-23OR010201024, “FY22 Report on ORNL Sibling Rod Testing Results,” within work package SF-23OR01020102 and is an update to the work reported in M2SF-22OR010201047, M2SF-21OR010201032, M2SF-19ORO010201026, and M2SF- 19OR010201028. As a part of DOE NE High Burnup Spent Fuel Data Project, Oak Ridge National Laboratory (ORNL) is performing destructive examinations (DEs) of high burnup (HBU) (>45 GWd/MTU) spent nuclear fuel (SNF) rods from the North Anna Nuclear Power Station operated by Dominion Energy. The SNF rods, called sister rods or sibling rods, are all HBU and include four different kinds of fuel rod cladding: standard Zircaloy-4 (Zirc-4), low-tin (LT) Zirc-4, ZIRLO, and M5. The DEs are being conducted to obtain a baseline of the HBU rods’ condition before dry storage and are focused on understanding overall SNF rod strength and durability. Composite fuel and defueled cladding will be tested to derive material properties. Although the data generated can be used for multiple purposes, one primary goal for obtaining the post-irradiation examination data and the associated measured mechanical properties is to support SNF dry storage licensing and relicensing activities by (1) addressing identified knowledge gaps and (2) enhancing the technical basis for post-storage transportation, handling, and subsequent disposition. This appendix documents an evaluation of the fatigue data to enhance the technical basis for post-storage transportation, handling, and subsequent disposition and to identify future testing needs for Phase 2 of the project.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Leveraging Artificial Intelligence in Federal Projects

Artificial intelligence (AI) has the potential to transform grid operations. As energy demand rises, weather patterns shift, and foreign threats to critical infrastructure grow, it is essential to harness advanced technology to modernize the grid and increase overall resiliency. This paper examines the integration of AI in federally funded grid infrastructure projects. By analyzing project data, it evaluates the penetration and use cases of AI technologies within key funding initiatives and explores opportunities and challenges associated with their deployment. This analysis categorizes AI adoption in recent federal grid investments, establishing a baseline for measuring near-term impacts and identifying promising AI applications. The research found that 16% of recent federal energy projects included AI integration, and approximately 75% of these projects used AI for more than one primary application.

24 - POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Soil Moisture Data for TRACER project (Houston, TX)

Three soil moisture and metrological data collection sites are located in the Houston, TX, area and collect data every 5 minutes. The data includes soil moisture, volumetric wate content, electrical conductivity of soil, soil temperature, rain precipitation, air temperature and other parameters. For real-time streaming of the data please visit the website: https://coastal.beg.utexas.edu/soilmoisture/#!/

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Sister Rod Destructive Examinations (FY2021)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Sister Rod Destructive Examinations (FY22)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

Sister Rod Destructive Examinations (FY20) Appendix B: Segmentation, Defueling, Metallograhic Data and Total Cladding Hydrogen

As a part of the US DOE-NE High Burnup Spent Fuel Data Project, ORNL is performing destructive examinations (DEs) of high burnup (>45 gigawatt days per metric ton uranium) spent nuclear fuel (SNF) rods from the North Anna Nuclear Power Station operated by Dominion Energy. The SNF rods, called the “sister rods,” are all HBU and include four different kinds of fuel rod cladding: standard Zircaloy-4, low-tin Zircaloy-4, ZIRLO®, and M5®. The DEs are being conducted to obtain a baseline of the HBU rod’s condition prior to dry storage and are focused on understanding overall SNF rod strength and durability. Both composite fuel and empty cladding are being tested. While the data generated can be used for multiple purposes, a primary goal for obtaining the post-irradiation examination data and the associated measured mechanical properties is to support SNF dry storage licensing and relicensing activities.This report documents the status of the ORNL Phase 1 DE activities related to: rough segmentation, defueling, optical microscopy, and cladding total hydrogen measurements for 7 Phase 1 sister rods and outlines the DE tasks performed and the data collected to date. The results of these detailed examinations and others are summarized in the Sister Rod DE Status Report.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

Sister Rod Destructive Examinations (FY20) Appendix A: Full Length Rod Heat Treatments (FHT)

As a part of the US DOE-NE High Burnup Spent Fuel Data Project, ORNL is performing destructive examinations (DEs) of high burnup (HBU) (>45 gigawatt days per metric ton uranium) spent nuclear fuel (SNF) rods from the North Anna Nuclear Power Station operated by Dominion Energy. The SNF rods, called the “sister rods,” are all HBU and include four different kinds of fuel rod cladding: standard Zircaloy-4, low-tin Zircaloy-4, ZIRLO®, and M5®. The DEs are being conducted to obtain a baseline of the HBU rod’s condition prior to dry storage and are focused on understanding overall SNF rod strength and durability. Both composite fuel and empty cladding are being tested. While the data generated can be used for multiple purposes, a primary goal for obtaining the post-irradiation examination data will support SNF dry storage licensing and relicensing activities.This report documents the status of the ORNL Phase 1 DE activities related to full length rod heat treatments (FHT) applied to selected sister rods in Phase 1 of the sister rod test program. The results of this work and detailed examinations are summarized in the Sister Rod DE Status Report.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Sister Rod Destructive Examinations (FY22) Appendix I: SNF Aerosols Released During Rod Fracture

As a part of the DOE NE High Burnup Spent Fuel Data Project, Oak Ridge National Laboratory (ORNL) is performing destructive examinations (DEs) of high burnup (HBU) (>45 GWd/MTU) spent nuclear fuel (SNF) rods from the North Anna Nuclear Power Station operated by Dominion Energy. The SNF rods, called sister rods or sibling rods, are all HBU and include four different kinds of fuel rod cladding: standard Zircaloy-4 (Zirc-4), low-tin Zirc-4, ZIRLO, and M5. The DEs are being conducted to obtain a baseline of the HBU rods’ condition before dry storage and are focused on understanding overall SNF rod strength and durability. Fuel rods and defueled cladding will be tested to derive material properties. Although the data generated can be used for multiple purposes, one primary goal for obtaining the post-irradiation examination data and the associated measured mechanical properties is to support SNF dry storage licensing and relicensing activities by (1) addressing identified knowledge gaps and (2) enhancing the technical basis for post-storage transportation, handling, and subsequent disposition. This report documents the status of the ORNL Phase 1 DE activities related to the collection of SNF aerosol particles released during fuel rod fracture in 4-point bending in Phase 1 of the sister rod test program.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Sister Rod Destructive Examinations (FY23) Appendix I: SNF Aerosols Released During Rod Fracture

This report documents work performed under the Spent Fuel and Waste Disposition’s Spent Fuel and Waste Science and Technology program for the US Department of Energy (DOE) Office of Nuclear Energy (NE). This work was performed to fulfill Level 3 Milestone M3SF-23OR010201026, “FY23 M3 draft report on results from testing in FY23,” within work package SF-23OR01020102 and is an updated to the work reported in M2SF-23OR010201024, M2SF-22OR010201047, M2SF-21OR010201032, M2SF-19ORO010201026, and M2SF-19OR010201028. As a part of the DOE NE High Burnup Spent Fuel Data Project, Oak Ridge National Laboratory (ORNL) is performing destructive examinations (DEs) of high burnup (HBU) (>45 GWd/MTU) spent nuclear fuel (SNF) rods from the North Anna Nuclear Power Station operated by Dominion Energy. The SNF rods, called sister rods or sibling rods, are all HBU and include four different kinds of fuel rod cladding: standard Zircaloy-4 (Zirc-4), low-tin Zirc-4, ZIRLO, and M5. The DEs are being conducted to obtain a baseline of the HBU rods’ condition before dry storage and are focused on understanding overall SNF rod strength and durability. Fuel rods and defueled cladding will be tested to derive material properties. Although the data generated can be used for multiple purposes, one primary goal for obtaining the post-irradiation examination data and the associated measured mechanical properties is to support SNF dry storage licensing and relicensing activities by (1) addressing identified knowledge gaps and (2) enhancing the technical basis for post-storage transportation, handling, and subsequent disposition.This report documents the status of the ORNL Phase 1 DE activities related to the collection of SNF aerosol particles released during fuel rod fracture in 4-point bending in Phase 1 of the sister rod test program.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

Sister Rod Destructive Examinations (FY20)

As a part of DOE NE High Burnup Spent Fuel Data Project, Oak Ridge National Laboratory (ORNL) is performing destructive examinations (DEs) of high burnup (HBU) (>45 GWd/MTU) spent nuclear fuel (SNF) rods from the North Anna Nuclear Power Station operated by Dominion Energy. The SNF rods, called sister rods or sibling rods, are all HBU and include four different kinds of fuel rod cladding: standard Zircaloy-4 (Zirc-4), low-tin Zirc-4, ZIRLO, and M5. The DEs are being conducted to obtain a baseline of the HBU rod’s condition before dry storage and are focused on understanding overall SNF rod strength and durability. Composite fuel and defueled cladding will be tested to derive material properties. Although the data generated can be used for multiple purposes, one primary goal for obtaining the post-irradiation examination data and the associated measured mechanical properties is to support SNF dry storage licensing and relicensing activities by (1) addressing identified knowledge gaps and (2) enhancing the technical basis for post-storage transportation, handling, and subsequent disposition. This report documents the status of the ORNL Phase I DEs of 8 sister rods and outlines the DE tasks performed and the data collected to date, as guided by the sister rod test plans.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗