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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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Computational methods underpin advancing the science and engineering of energy efficiency, sustainable transportation, renewable power technologies, and developing a knowledge base to optimize energy systems. NREL's Computational Science Center (CSC) proudly focuses on providing the service of computing, advancing the science of computing, and enabling NREL's clean energy mission.
The National Renewable Energy Laboratory (NREL) is the U.S. Department of Energy's primary national laboratory for energy systems research and development. As the energy systems laboratory, NREL's unique strength lies in developing and integrating a broad array of energy technologies into robust, resilient systems - bridging foundational research with practical applications to lower energy costs, drive economic growth, bolster national security, and deliver abundant and reliable energy.
The National Renewable Energy Laboratory (NREL) is a leader in advancing hydropower technologies, positioning hydropower as a pillar of an affordable, reliable, and secure energy future. Through its hydropower program, NREL is conducting innovative research that reduces energy costs, drives efficiency, and supports systems integration - unlocking economic opportunities, rebuilding supply chains, and fueling America's global competitiveness.
The comparison of the absolute cavity pyrgeometers (ACPs) with the InfraRed Integrating Sphere (IRIS), Eppley Precision Infrared Radiometer (PIR) pyrgeometers, and Kipp & Zonen (KZ) pyrgeometers traceable to the World Infrared Standard Group (WISG) was held during NREL ACP and IRIS Comparisons (NAIC) from September 25 to October 6, 2023. Data from all instruments was collected during nighttime clear sky conditions only. The irradiance measured by the ACPs is collected in 30 seconds intervals during the measurement period of two hours, and 10 seconds intervals during the calibration period of 6 minutes. During the comparison, the average (av) irradiance difference measured by ACPs and IRIS9 varied from -0.75 W/m 2 to 0.76 W/m 2 , standard deviation (sd) from 0.78 W/m 2 to 1.04 W/m 2 , and uncertainty U95 from 1.96 W/m 2 to 2.07 W/m 2 . The average irradiance difference measured by ACP95F3 minus the irradiance measured by all pyrgeometers varied from 1.64 to 3.96 W/m 2 , sd from 1.70 W/m 2 to 1.86 W/m 2 , and uncertainty U 95 from 3.78 W/m 2 to 5.42W/m 2 . Note that from September 25th at 18:31 to September 29 th at 5:30 ACP96F3 irradiance is calculated using Bruce, et al 2023 method.
The objective of this project is to enable evaluation of a Southern Company distribution feeder on the Advanced Distribution Management System (ADMS) test bed. The long-term goal is to evaluate a federated distributed energy resource (DER) management solution that aggregates DERs through either direct control, transactive control or an aggregator to provide bulk services while observing distribution system voltage and power constraints. The DER aggregation needs to be coordinated with an ADMS that is responsible for reliable power delivery across the distribution systems. This project takes the first step towards enabling such evaluation by deploying an ADMS from Oracle (Southern Company's ADMS supplier) with a Southern Company feeder at NREL.
These data include raw scanning Doppler lidar measurements from the deployment of the NREL Halo XR+ (s/n 235) at WFIP3's BARG. The raw measurements include uncalibrated beam azimuth angles, radial velocity, backscatter, signal to noise ratio per each line of sight, and range-gate. Note: the measurements have NOT been corrected for the motion of the barge.
This dataset contains raw data from NREL's scanning lidar performing vertical profiles at the BLOC site. This lidar was Halo XR #216 through February 24, 2025, Halo XR #217 from February 24, 2025 through April 17, 2025, and again Halo XR #216 after that.
This dataset contains processed data from NREL's scanning lidar performing vertical profiles at the BLOC site. This lidar was Halo XR #216 through February 24, 2025, Halo XR #217 from February 24, 2025 through April 17, 2025, and again Halo XR #216 after that.
This dataset contains raw data from NREL's profiling lidar (Windcube v2.1) at the WFIP3 BARG site. Two data types are included here: 1) STA files, which have 10-minute average data, and 2) RTD files, which have real time data, at about 1 Hz resolution. Note: these data have NOT been corrected for the motion of the barge.
This dataset contains processed data from NREL's Halo XR+ scanning lidar at WFIP3's BARG site. Note: this version of the data has NOT been corrected for the motion of the barge.
These data include raw scanning Doppler lidar measurements from the deployment of the NREL Halo XR+ (s/n 235) at WFIP3's NOAA ship. The raw measurements include uncalibrated beam azimuth angles, radial velocity, backscatter, signal to noise ratio per each line of sight, and range-gate. The location of the ship is provided separately.
This dataset contains processed data from NREL's Halo XR+ #235 scanning lidar at WFIP3's NOAA ship site. The location of the ship is provided separately. The data have NOT been corrected for the motion of the ship.
This dataset contains raw data from NREL's profiling lidar (Windcube v2.1) at the WFIP3 NOAA SHIP site. Two data types are included here: 1) STA files, which have 10-minute average data, and 2) RTD files, which have real time data, at about 1 Hz resolution.
These data include raw scanning Doppler lidar measurements from the deployment of the NREL HALO XR+ (s/n 235) at the A1 site. The raw measurements include uncalibrated beam azimuth angles, radial velocity, backscatter, signal to noise ratio per each line of sight, and range-gate.
This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe v0.12 (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 minutes from instantaneous observations from the NREL ASSIST-II (SN 10) infrared spectrometer. Observations are noise-filtered but not averaged in time to minimize errors due to non-uniform clouds. Additional input data in TROPoe are cloud base height from a Vaisala CL51 ceilometer. The full pipeline for running the retrieval is available at https://github.com/StefanoWind/TROPoe_processor. Met data were not ingested. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) that provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see, e.g., Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at Denver, CO.
This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe v0.12 (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 minutes from instantaneous observations from the NREL ASSIST-II (SN 12) infrared spectrometer. Observations are noise-filtered but not averaged in time to minimize errors due to non-uniform clouds. Additional input data in TROPoe are cloud base height from a Vaisala CL51 ceilometer. The full pipeline for running the retrieval is available at https://github.com/StefanoWind/TROPoe_processor. Met data were not ingested. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) that provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see, e.g., Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at Denver, CO.
This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe v0.12 (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 minutes from instantaneous observations from the NREL ASSIST-II (SN 11) infrared spectrometer. Observations are noise-filtered but not averaged in time to minimize errors due to non-uniform clouds. Additional input data in TROPoe are cloud base height from a Vaisala CL51 ceilometer. The full pipeline for running the retrieval is available at https://github.com/StefanoWind/TROPoe_processor. Met data were not ingested. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) that provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see, e.g., Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at Denver, CO.
This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe v0.12 (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 minutes from instantaneous observations from the NREL ASSIST-II (SN 11) infrared spectrometer. Observations are noise-filtered but not averaged in time to minimize errors due to non-uniform clouds. Additional input data in TROPoe are cloud base height (CBH), which is a combined data product that uses data from ceilometers at sites A1 and H and scanning lidars from ARM sites C1 and E37. The CBH is weighted inversely proportionally to the distance to the respective site to take into account the spatial variability of clouds (see https://github.com/StefanoWind/ASSIST_analysis/blob/main/awaken_processing/combine_cbh.py). The full pipeline for running the retrieval is available at https://github.com/StefanoWind/TROPoe_processor. Met data was not ingested. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) that provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see, e.g., Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at ARM SGP, OK.