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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Utah FORGE Well 16A(78)-32 Simplified Discrete Fracture Network Data

The FORGE team is making these fracture models available to researchers wanting a set of natural fractures in the FORGE reservoir for use in their own modeling work. They have been used to predict stimulation distances during hydraulic stimulation at the open toe section of well 16A(78)-32. This is a simplified DFN (discrete fracture network) dataset, that was generated using FracMan, for Utah FORGE well 16A(78)-32. A short, well-illustrated, report describing the data is also included in the provided archive file.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Data Arrays for Microearthquake (MEQ) Monitoring using Deep Learning for the Newberry EGS Sites

The 'Machine Learning Approaches to Predicting Induced Seismicity and Imaging Geothermal Reservoir Properties' project looks to apply machine learning (ML) methods to Microearthquake (MEQ) data for imaging geothermal reservoir properties and forecasting seismic events, in order to advance geothermal exploration and safe geothermal energy production. As part of the project, this submission provides data arrays for 149 microearthquakes between the year 2012 and 2013 at the Newberry EGS Site for use with the Deep Learning Algorithm that has been developed. The data provided includes raw waveform data, location data, normalized waveform data, and processed waveform data. Penn State Geothermal Team has shared the following files from the project: - 149 microearthquakes (MEQs) between 2012 and 2013 at Newberry EGS sites, 'Normalized Waveform Inputs.npz' are normalized waveforms. - labels of 149 MEQs: Processed Waveform Inputs.npz - location labels of 149 MEQs: Location Data.npz Note: .npz is the python file format by NumPy that provides storage of array data.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Appendices for Geothermal Exploration Artificial Intelligence Report

The Geothermal Exploration Artificial Intelligence looks to use machine learning to spot geothermal identifiers from land maps. This is done to remotely detect geothermal sites for the purpose of energy uses. Such uses include enhanced geothermal system (EGS) applications, especially regarding finding locations for viable EGS sites. This submission includes the appendices and reports formerly attached to the Geothermal Exploration Artificial Intelligence Quarterly and Final Reports. The appendices below include methodologies, results, and some data regarding what was used to train the Geothermal Exploration AI. The methodology reports explain how specific anomaly detection modes were selected for use with the Geo Exploration AI. This also includes how the detection mode is useful for finding geothermal sites. Some methodology reports also include small amounts of code. Results from these reports explain the accuracy of methods used for the selected sites (Brady Desert Peak and Salton Sea). Data from these detection modes can be found in some of the reports, such as the Mineral Markers Maps, but most of the raw data is included the DOE Database which includes Brady, Desert Peak, and Salton Sea Geothermal Sites.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Utah FORGE Well 16A(78)-32 Core Photos

Images of core samples collected from Utah FORGE well 16A(78)-32. These images were created by stitching together multiple photographs resulting in a circumferential view of the cores exterior in two dimensions. Core footages (measured depths) are indicated in the file names, and are annotated on each image. The images, of which there are 30 in the .zip file, are in a .jpg format.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Utah FORGE InSAR Data from 2020

Interferometric Synthetic Aperture Radar data from the TerraSAR-X and the TanDEM-X satellite missions operated by the German Space Agency (DLR). Interferometric pairs (interferograms) were created using generic mapping tool GMT-SAR processing software (see link in Resources). Data from January through December 2020.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Utah FORGE InSAR Data from 2021

Interferometric Synthetic Aperture Radar data from the TerraSAR-X and the TanDEM-X satellite missions operated by the German Space Agency (DLR). Interferometric pairs (interferograms) were created using generic mapping tool GMT-SAR processing software (see link in Resources). Data from January through November 2021.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Utah FORGE InSAR Data from 2022

Interferometric Synthetic Aperture Radar data from the TerraSAR-X and the TanDEM-X satellite missions operated by the German Space Agency (DLR). Interferometric pairs (interferograms) were created using generic mapping tool GMT-SAR processing software (see link in Resources). Data from January through June 2022.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Utah FORGE: InSAR Data Best Pairs

This submission provides Interferometric Synthetic Aperture Radar (InSAR) data covering the Utah FORGE site via the TerraSAR-X and TanDEM-X satellite missions operated by the German Space Agency (DLR). Data was collected between 2019/01/01 and 2023/06/30. Interferometric pairs (interferograms) were created using generic mapping tool GMT-SAR processing software. The best 112 pairs were selected based on having short orbital separations (perpendicular baseline less than 5 meters in absolute value).

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Utah FORGE: 2023 Large Upscaled Discrete Fracture Network Models

This dataset includes the data and a report on the large upscaled discrete fracture network modeling done for the Utah FORGE project in 2023. The FORGE modeling team is making five discrete fracture network (DFN) realizations of a large reservoir model available to researchers. These models have been upscaled to a continuum mesh or grid at resolutions of 10 meters and 20 meters providing reservoir properties for fracture porosity, permeability, and compressibility. The models are available in both the reference global coordinate frame and a local coordinate frame aligned with principal stress directions.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Utah FORGE: Interferometric Synthetic Aperture Radar Data from 2023 and 2024

The dataset comprises Interferometric Synthetic Aperture Radar (InSAR) data from the TerraSAR-X and TanDEM-X satellite missions, covering the Utah FORGE site. This data includes interferometric pairs created using GMT-SAR processing software, chosen for their short orbital separations between May 1, 2023, and June 30, 2024. Included are various data and metadata, including Digital Elevation Models, unit vectors, and correlation coefficients. The dataset is packaged in several compressed tar files and formatted in NetCDF. To utilize this dataset, users will need software capable of handling NetCDF files and tools for decompressing tar files.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

FleetREDI Insight: Beverage Delivery in New York City

Capturing real-world data is critical to improving efficiency and supporting technology advancements in commercial vehicles. FleetREDI’s insights provide detailed duty cycle information and highlight unique aspects of the given dataset. Each insight delivers a quick look at the collected data by summarizing the operation and identifying key findings of the initial analysis. This FleetREDI insight explores beverage delivery tractors operating in New York City. Last-mile beverage delivery supports local bars and restaurants throughout Manhattan and the broader New York City area. Manhattan Beer Distributors is a beverage delivery company operating in Manhattan and the Bronx. Logging devices were installed in 17 vehicles, and operational data were collected between August and October 2022. Two types of vehicles were included in data collection: 7 tractors and 10 bay trucks. Using NLR’s FleetREDI data platform, this dataset provides a summary of daily operation to help understand duty cycle characteristics. This includes daily distance, fuel use, and estimated engine-produced energy consumption for 17 bay trucks and tractors that operated more than 7,500 miles in slow-speed urban operation. ![FleetREDI beverage delivery](FleetREDI-beverage-delivery-nyc.jpg)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

FleetREDI Insight: Intrastate Coach Bus Dataset

Capturing real-world data is critical to improving efficiency and supporting technology advancements in commercial vehicles. FleetREDI’s insights provide detailed duty cycle information and highlight unique aspects of the given dataset. Each insight delivers a quick look at the collected data by summarizing the operation and identifying key findings of the initial analysis. This FleetREDI insight explores coach buses operating in Colorado. Coach buses are a primary mover for intrastate transit and are primarily used for longer trips with more comfortable seats and a restroom. All Aboard America! Holdings Inc. offers various fixed-service and charter routes across Colorado on its Bustang fleet out of its depot in Golden, Colorado. NLR installed logging devices and collected operational data on nine 40-foot Bustang motorcoaches operating on fixed routes from May through August 2022. Using NLR’s FleetREDI data platform, this dataset provides a summary of daily operation to help understand duty cycle characteristics. This includes daily distance, fuel use, and estimated engine-produced energy consumption for nine motorcoaches that operated more than 33,000 miles. These vehicles primarily operated on Interstate 25 and Interstate 70. ![FleetREDI interstate bus](FleetREDI-interstate-bus.jpg)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Report of the Condition of a General Electric Transportation Systems Gearbox

Wind power plant operations and maintenance (O&M) costs remain an appreciable contributor to the overall cost of wind energy. Premature of wind turbine gearboxes failures are often a result of abrasive wear, micropitting, scuffing, white-etch cracks, and macropitting issues. Micropitting is a fatigue phenomenon that occurs in Hertzian contacts in both gears and rolling-element bearings that operate in mixed or micro elastohydrodynamic lubrication regimes. Micropitting is influenced by and manifests itself in many different ways depending on operating conditions such as load, speed and operating temperature, and on factors such as gear geometry and accuracy, tooth flank roughness, percentage of tooth sliding, and lubricant composition. Despite much research by many investigators, micropitting remains complex, unpredictable, and difficult to control and remains a problem in wind turbine gearboxes. The purpose of this report is to document and describe the micropitting damage observed in a wind turbine gearbox, thereby facilitating further analysis and examination in future reports.

17 WIND ENERGY↗
DOE OSTI2021

Dependence of Dust Formation on the Supernova Explosion and nuDust

Dust is ubiquitous in the interstellar medium, serving as both a source of information and headaches to astronomers, depending on who you ask. The out-flows of core collapse supernovae (CCSNe) serve as a factory for dust nucleation and are one of a few known sources of dust in the universe. From observations of local CCSNe such as 1987A and Cassiopeia A, we know that dust is abundant in CCSN ejecta prior to interacting with the ISM. Furthermore, the explosion energy, explosive engine, metallicity, and progenitor mass of the CCSN will all impact the subsequent dust formation history and composition. Observations of the supernova ejecta probe the detailed composition of the ejecta which, in turn, can be used probe the properties of the progenitor star and the process of the explosion

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Program Overview Laboratory Directed Research and Development FY2020 (Annual Report)

Throughout this year’s report, we highlight how the LDRD program invests in ideas that make the world a safer place. We review key accomplishments and performance indicators, and we share snapshots of some of our talented staff. I also encourage you to visit our LDRD website and learn more about the 244 projects that we supported during fiscal year 2020. Looking to the future, I am confident that LDRD investments will continue to help LLNL remain at the forefront of innovative research.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗