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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 181 records · Page 10

WFIP3 - BLOC site - CU Profiling Lidar (Windcube v1) / Raw data

This dataset is from WFIP3 BLOC site WindCube v1 Profiling Lidar / Raw Data from CU Boulder. Please note that the .sta files are in human-readable text, while the .rtd files require some processing software that is not easily automated. We plan to process the .rtd data following the campaign, but if you wish to try it sooner, we can make it available upon request.

17 WIND ENERGY↗

Raw data for publication: Cao et al. 2023. GCB-Bioenergy (accepted for publication).

Raw data for publication: Viet Dang Cao, Baskaran Kannan, Guangbin Luo, Hui Liu, John Shanklin, and Fredy Altpeter. 2023. Triacylglycerol, total fatty acid and biomass accumulation of metabolically engineered energycane grown under field conditions. GCB-Bioenergy (accepted for publication).

Bioenergy, energycane, lipids, biodiesel, biofuel,↗

UNH TDP - ADV Raw Data and Processing Scripts - Fall 2021

This submission contains raw Acoustic Doppler Velocimeter (ADV) data and processing scripts associated with MHKDR submission 394 (UNH TDP - Concurrent Measurements of Inflow, Power Performance, and Loads for a Grid-Synchronized Vertical Axis Cross-Flow Turbine Operating in a Tidal Estuary, DOI: 10.15473/1973860) from the University of New Hampshire and Atlantic Marine Energy Center (AMEC) turbine deployment platform. The user is directed to the MHKDR submission 394 for relevant context and detail of this deployment; see link below. The 394_READ_ME file here provides the description from that submission for quick reference. The READ_ME file for this specific instrument from the 394 submission is also available here. This submission contains a zipped folder structure containing raw data in its original format and MATLAB (2019a) processing scripts used to process and manipulate the data into its final form. The final data products are submitted in the 394 submission.

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UNH TDP - ADCP Raw Data and Processing Scripts - Fall 2021

This submission contains raw Acoustic Doppler Current Profiler (ADCP) data and processing scripts associated with MHKDR submission 394 (UNH TDP - Concurrent Measurements of Inflow, Power Performance, and Loads for a Grid-Synchronized Vertical Axis Cross-Flow Turbine Operating in a Tidal Estuary, DOI: 10.15473/1973860) from the University of New Hampshire and Atlantic Marine Energy Center (AMEC) turbine deployment platform. The user is directed to the MHKDR submission 394 for relevant context and detail of this deployment; see link below. The 394_READ_ME file here provides the description from that submission for quick reference. The READ_ME file for this specific instrument from the 394 submission is also available here. This submission contains a zipped folder structure containing raw data in its original format and MATLAB (2019a) processing scripts used to process and manipulate the data into its final form. The final data products are submitted in the 394 submission.

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UNH TDP - IMU Raw Data and Processing Scripts - Fall 2021

This submission contains raw Yost IMU (Inertial Measurement Unit) data and processing scripts associated with MHKDR submission 394 (UNH TDP - Concurrent Measurements of Inflow, Power Performance, and Loads for a Grid-Synchronized Vertical Axis Cross-Flow Turbine Operating in a Tidal Estuary, DOI: 10.15473/1973860) from the University of New Hampshire and Atlantic Marine Energy Center (AMEC) turbine deployment platform. The user is directed to the MHKDR submission 394 for relevant context and detail of this deployment; see link below. The 394_READ_ME file here provides the description from that submission for quick reference. The READ_ME file for this specific instrument from the 394 submission is also available here. This submission contains a zipped folder structure containing raw data in its original format and MATLAB (2019a) processing scripts used to process and manipulate the data into its final form. The final data products are submitted in the 394 submission.

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Soil temperature and soil moisture raw data, permafrost table depths, and accompanying environmental variable data, Kenai Wildlife Refuge, 2019-2022

Data package purpose: This data package was created to contain all data used in an upcoming article, "Canopy Cover and Microtopography Control Precipitation-Enhanced Thaw of Ecosystem-Protected Permafrost." In review.This data package includes: Raw output from 19 distributed temperature profilers with a thermistor every 10 cm along a 160 cm length at a measurement interval of 15 minutes (.CSV). Raw output from two soil moisture and temperature profilers (90 cm length and 120 cm length) that took composite soil moisture readings every 15 cm along the sensor length at a measurement interval of 30 minutes (.CSV). Permafrost depths were measured annually in mid-September at DTP sensor locations (.CSV) and along an across-site transect (.CSV). Environmental variables (snow depth, canopy closure, moss depth, and elevation) for all sensor locations. Real-time kinetic (RTK) GPS points showing site microtopography (.CSV).Analysis software: Our analysis was done in Matlab. File types can be used with any software.

54 ENVIRONMENTAL SCIENCES↗

Raw Data

This dataset contains high-frequency (10Hz) data from the GX5-45 IMU on the Barge Science vans. The data are all raw binary files.

17 WIND ENERGY↗

A Data-Fusion Method using Bayesian Approach to Enhance Raw Data Accuracy of Position and Distance Measurements for Connected Vehicles

Accurate positioning of vehicles is a critical element of autonomous and connected vehicle systems. Most of other studies heavily focused on enhancing simultaneous localization and mapping (SLAM) methods, i.e., constructing or updating a map of an unknown environment and tracking an object within the map. This paper provides a method that can, in addition to existing SLAM or relevant methods, enhance the raw measurements of position and distance. The basic idea of this study is to identify and update the error distribution of each data source by combining all available information. A Bayesian approach was incorporated to estimate and update the error distribution of individual data sources or sensors. The proposed method can be conducted in real-time environments, and a self-learning scheme determines whether enough data has been collected to further improve the accuracy of such measurements. The simulated experiments show that the proposed model noticeably improves the accuracy of position and distance measurements. Especially, the estimated biases of position coordinates and distance measures are very close to the biases of true error distributions, with the R-squared over 0.98. A similar approach can also be utilized to enhance accuracy of other sensors or measurements in connected vehicle or relevant systems, where multi-data sources are available.

Lim, Hyeonsup↗

Raw Data for: Ultra-Confined Environments May Restrict the Possible Configurations of Supported Metal Complexes

Raw NMR data (1D 2H spectrum and pseudo-2D 2H CODEX spectra) in Bruker topspin format. Raw DRIFTS IR data as text files. Results from the DFT potential energy surface scans (coordinates are in the supporting information of the main publication). Raw BET nitrogen physisorption isotherms and the determined more size distributions.

Perras, Frederic A [Ames National Laboratory]↗

Photoelectrochemically Self Improving Si/GaN Photocathode: Figure 1a Raw Data

10 hours chronoamperometry (CA) testing under 1 sun illumination and constant bias at -0.6 V vs RHE, the corresponding Faradaic efficiency reveals a self-improving nature of GaN, inset: CA testing on bare Si for 5 hours under 1 sun illumination and -0.6 V vs RHE, this bare Si photocathode rapidly drops down to < 0.05 mA/cm2 within an hour. Both CA testing performed in 0.5 M H2SO4 (pH=0.4).

photocathode↗

Photoelectrochemically Self Improving Si/GaN Photocathode: Figure 1c Raw Data

Intermittent chronoamperometry (CA) testing was performed on Si/GaN photocathode in hourly manner. The LSV scan at 0 hour, 1 hour CA, 2 hours CA, 3 hours CA, 4 hours CA, 6 hours CA, 8 hours CA and 10 hours CA were recorded to track the changes of the photoelectrochemica (PEC) performance of the photocathode. The scan range was set to be 0V vs open circuit voltage (Eoc) to -0.7V vs RHE. Test was performed under one sun illumination in 0.5M H2SO4 (pH=0.4).

photocathode↗

HERO WEC V1 Upgrade - 2023 Laboratory Testing (Raw Data)

This submission contains the original, unprocessed data from the 2023 Large Amplitude Motion Platform (LAMP) testing of NREL's Hydraulic and Electric Reverse Osmosis Wave Energy Converter (HERO WEC). This data serves as a companion to MHKDR #520. Data was collected using NREL's Modular Ocean Data AcQuisition (MODAQ) system in TDMS format. Specifications of TDMS files can be found on the NI website. The TDMS files have been separated into zip files corresponding to either Drivetrain, Hydraulic, or Electric configuration runs representing the respective test cases that were run. The drivetrain runs were used to characterize the drivetrain only (no pump or generator). The Hydraulic runs represent the configuration when the seawater pump is installed, and the Electric runs represents the configuration when the generator is installed. The following sub-categories of data are included for each type: - DW - Deep water (monochromatic sine wave) profile (not run in drivetrain configuration) - Heave - Heave only (monochromatic sine wave) profile - Heave_NoRO (hydraulic configuration only) - Heave_ACC (hydraulic configuration only) - IR - Surge and heave irregular wave profile (not run in drivetrain configuration) - RW - Heave only profile created from real world encoder data (not run in drivetrain configuration) Reference documents: - "HERO WEC Lamp Test Run Log.xlsx": contains specifications for each test run - "Lamp Data Description.docx": provides detailed information about data types and processing methods For those interested in the processed data the authors have created a separate submission, MHKDR #520, linked below. This data set has been developed by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Water Power Technologies Office.

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