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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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This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-xBN NEON Caribou Creek - Poker Flats Watershed (BONA). This is the FLUXNET version of the carbon flux data for the site US-xBN NEON Caribou Creek - Poker Flats Watershed (BONA) produced by applying the standard ONEFlux (1F) software.
This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-ASH USSL San Joaquin Valley Almond High Salinity. This is the FLUXNET version of the carbon flux data for the site US-ASH USSL San Joaquin Valley Almond High Salinity produced by applying the standard ONEFlux (1F) software.
This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-ASM USSL San Joaquin Valley Almond Medium Salinity. This is the FLUXNET version of the carbon flux data for the site US-ASM USSL San Joaquin Valley Almond Medium Salinity produced by applying the standard ONEFlux (1F) software. Site Description - almond orchard in San Joaquin Valley. Different water source and water quality than adjacent site US-SAH
This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-SP1 Slashpine-Austin Cary- 65yrs nat regen. This is the FLUXNET version of the carbon flux data for the site US-SP1 Slashpine-Austin Cary- 65yrs nat regen produced by applying the standard ONEFlux (1F) software. Site Description - The ACMF site is a 67 hectare naturally regenerated Pinus palustris and Pinus elliottii mixed stand.
This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-xNW NEON Niwot Ridge Mountain Research Station (NIWO). This is the FLUXNET version of the carbon flux data for the site US-xNW NEON Niwot Ridge Mountain Research Station (NIWO) produced by applying the standard ONEFlux (1F) software. Site Description - The Niwot Ridge sits approximately 27 km west of Boulder, Colorado, and 6 km east of the Continental Divide. Topography, climate, and biota of the site are representative of Rocky Mountain alpine ecosystems, including extensive alpine tundra (mostly herbs, some shrubs and scree) and subalpine coniferous forests (Abies lasciocarpa and Picea engelmanii at higher elevations), talus slopes, wetlands and a variety of glacial landforms. Characterized by cold and relatively long winters, Niwot Ridge has an average annual temperature of 1.5°C and average annual precipitation of 800 mm. Most precipitation falls as snow and summer precipitation falls primarily during afternoon thunderstorms. Located on the eastern side of the Continental Divide at 3,000-3,500 m elevation, the site best captures chemical inputs produced along the Front Range and is well situated to observe other east/west flows across the Southern Rockies in conjunction with other NEON sites.
This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site CA-SF3 Saskatchewan - Western Boreal, forest burned in 1998. This is the FLUXNET version of the carbon flux data for the site CA-SF3 Saskatchewan - Western Boreal, forest burned in 1998 produced by applying the standard ONEFlux (1F) software. Site Description - The 1998 burn site (F98) was in the east part of Prince Albert National Park, Saskatchewan, in the Waskesiu Fire, ignited by lightning that burned about 1700 ha in July 1998. The pre-fire forest consisted of jack pine and black spruce stands, with some intermixed aspen. The fire was severe, consuming much of the top layer of organic soil and killing all trees. In 2001, much of the regenerating vegetation consisted of aspen saplings about 1 m tall and shorter jack pine and black spruce seedlings. An overstory of dead, leafless jack pine trees dominated at a height of 18 m. Sparse grass and herbs, such as fireweed (Epilobium angustifolium L.) covered the ground. There were a large number of fallen dead trees, mostly perched above the ground and not decomposing quickly.
This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-LS1 San Pedro River Lewis Springs Sacaton Grassland. This is the FLUXNET version of the carbon flux data for the site US-LS1 San Pedro River Lewis Springs Sacaton Grassland produced by applying the standard ONEFlux (1F) software. Site Description - This is a riparian grassland located along an old alluvial terrace along the banks of the entrenched San Pedro River in southeastern Arizona. Site climate is semiarid with monsoonal (Jul-Sep) rainfall. The grasses access groundwater (depth of 2-4 m)from an alluvial aquifer recharged elsewhere. Site water balance is not closed and ET greatly exceeds precipitation. The perennial bunchgrasses use both rainfall, groundwater and occasional flood water. This groundwater-using ecosystem is far more ebulliant and productive than the upland vegetation around it (shrublands and grasslands) outside of the San Pedro River corridor.
This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site MX-Aog Alamos Old-Growth tropical dry forest. This is the FLUXNET version of the carbon flux data for the site MX-Aog Alamos Old-Growth tropical dry forest produced by applying the standard ONEFlux (1F) software. Site Description - This tower is located at a patch of remnant old growth tropical dry forest where the dominant vegetation are leguminous trees and a notorious presence of the genus Brusera. The site is at about 18 km from the municipality of Alamos Sonora Mexico within a private ranch managed by the NGO Nature Culture International. The area is also within a federally protected land named “Area de Proteccion de Flora y Fauna Sierra de Alamos Rio-Cuchijaqui" in the catalog of the “Comision Nacional de Areas Naturales Protegidas (CONANP-Mexico)”. The preserved ranch is surrounded by a mosaic of forest patches with different successional stages of tropical dry forest, and some places that are used for local agriculture and livestock. This ecosystem lies in a highly seasonal region under the influence of the North American Monsoon that brings about 70% of the rains from July to September.
This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-UiG University of Illinois Maize-Soy 2. This is the FLUXNET version of the carbon flux data for the site US-UiG University of Illinois Maize-Soy 2 produced by applying the standard ONEFlux (1F) software. Site Description - Agricultural field planted with maize in a three year rotation with soy (maize-maize-soy) as a control site for Us-UiC when basalt began to be applied to Us-UiC in 2017. The first soy rotation was in 2019. This field is typically planted in May and harvested in October. This site is located at an experimental farm approximately 2 miles south of the University of Illinois at Urbana Champaign and is colocated with (500-1000m distance) all other Us-Ui sites.
This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site BR-ITA MataFLUX: Multi-species tree restoration planting. This is the FLUXNET version of the carbon flux data for the site BR-ITA MataFLUX: Multi-species tree restoration planting produced by applying the standard ONEFlux (1F) software. Site Description - This tower is situated at the Itatinga Research Station (USP-ESALQ), located within the Brazilian Atlantic Forest. The station focuses on promoting forest research and features a landscape consisting of eucalyptus plantations and native species restoration areas.
This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site CA-TP2 Ontario - Turkey Point 1989 Plantation White Pine. This is the FLUXNET version of the carbon flux data for the site CA-TP2 Ontario - Turkey Point 1989 Plantation White Pine produced by applying the standard ONEFlux (1F) software. Site Description - Plantation established in 1989 over sandy agriculture land
This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site CA-TP4 Ontario - Turkey Point 1939 Plantation White Pine. This is the FLUXNET version of the carbon flux data for the site CA-TP4 Ontario - Turkey Point 1939 Plantation White Pine produced by applying the standard ONEFlux (1F) software. Site Description - White pine plantation established in 1939 over sandy abandoned land
At the close of each fiscal year, the US Department of Energy (DOE) Sustainability Performance Division (SPD) issues guidance documents and technical resource aids/tools necessary for DOE sites and national laboratories to complete sustainability reporting requirements. SPD is part of the DOE Office of Asset Management. As required by DOE Order 436.1, Departmental Sustainability, “each site will develop and commit to an annual Site Sustainability Plan (SSP) that identifies its respective contribution toward meeting the DOE’s sustainability goals.” SPD collects and compiles information reported by each site to develop an agency-wide Sustainability Report and Implementation Plan, which is used to report DOE sustainability progress to the federal government as required by all major federal agencies. DOE launched a formal Sustainability Office and annual SSP process in 2011. Each year, Oak Ridge National Laboratory (ORNL), in concert with the Office of Science (SC), provides the resources essential to fulfill its commitment to deliver a complete and accurate SSP report and quality performance data for entry into the DOE Sustainability Dashboard as managed by SPD. The performance data entered by each DOE site are then combined to disclose the progress of each DOE Program Office and are further combined to show comprehensive progress for the agency. The Office of Asset Management provides assistance to program offices in sustaining their missions, freeing up resources by reducing waste, avoiding excess expenditure on utilities, maximizing productivity, and improving the efficiency of facilities and processes. By focusing on mission needs, programs and associated DOE sites can help the agency meet its sustainability goals, as outlined in federal statutory and regulatory requirements. In FY 2022, the SSP guidance was updated to capture requirements from Executive Order (EO) 14008, Tackling the Climate Crisis at Home and Abroad, the Energy Act of 2020 (EAct 20), actions outlined in DOE’s Climate Adaptation & Resilience Plan and Sustainability Plan, and EO 14057, Catalyzing Clean Energy Industries and Jobs Through Federal Sustainability. Updates in SSP guidance help to minimize and streamline reporting while simultaneously addressing updated federal requirements. Per DOE, each SSP report should provide an overview of the site’s planned actions, as well as an overview of efforts and accomplishments during the reporting period. SPD collects and compiles information reported by each site to develop DOE’s Annual Sustainability Report, Climate Adaptation & Resilience Plan, and Annual Energy Management Report to Congress. The agency goal has been to lower the reporting burden for sites and increase and improve the consistency of information available to decision makers, allowing them to better identify projects and potential for increased efficiency, as well as to reduce waste, lower emissions, and enhance operational resilience. Sites may elect to produce a more polished publication for their leadership and stakeholders, but this step is no longer required. The ORNL SSP narrative report (this document) and the reporting of DOE SPD Sustainability Dashboard performance data is a collaborative effort of approximately 30 subject matter experts (SMEs) from ORNL facility management and research divisions. Annually, these associates come together to provide a report that can be used by DOE to demonstrate continued agency progress in energy efficiency and sustainable federal operations.
This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-xUK NEON The University of Kansas Field Station (UKFS). This is the FLUXNET version of the carbon flux data for the site US-xUK NEON The University of Kansas Field Station (UKFS) produced by applying the standard ONEFlux (1F) software. Site Description - Along an eastern deciduous forest and tallgrass prairie transition zone just north of Lawrence, Kansas, this 6 km2 terrestrial field site is located at the University of Kansas Field Station. A 116' tall flux tower rises out of a mixed hardwood forest dominated by white ash trees. Field obdservations of plants, animals, soils and microbes are collected around the tower and in the nearby tallgrass prairie.
This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-DFC US Dairy Forage Research Center, Prairie du Sac. This is the FLUXNET version of the carbon flux data for the site US-DFC US Dairy Forage Research Center, Prairie du Sac produced by applying the standard ONEFlux (1F) software. Site Description - Former US ammunition plant converted to dairy farm in 1980 with annual and perennial croplands mostly comprised of alfalfa, corn, soybean and wheat, with natural vegetation (forest, grass and shrubland), as well as pastures and and hedgerows surrounding the fields. During spring and summer months, dry cows and heifer graze on pastures.
This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site CA-Ca3 British Columbia - Pole sapling Douglas-fir stand. This is the FLUXNET version of the carbon flux data for the site CA-Ca3 British Columbia - Pole sapling Douglas-fir stand produced by applying the standard ONEFlux (1F) software. Site Description - Site: 1988 Harvested Douglas Fir (Pole-sapling); Tower Type 10" triangular tower; Latitude/Longitude 49.53462N -124.90044W (GPS survey, 2003. Coordinates updated/corrected May, 2006); Elevation approx. 170 m; Mean Air Temp. (for the growing season) 9.7 C *; Mean Annual Precipitation 1179 mm *; Date Operations Began: September 2001; Frequency of EC Measurements 20 Hz; Frequency of Other Met. Sensors 4 sec, year round; Soil Respiration Measurements Periodically during growing season using portable system in 2002; * 30-yr climate normals from the Comox Airport
These data are collected by a Streamline XR Doppler Lidar operated by Lawrence Livermore National Laboratory and deployed at the Tree Tops site (1.5 km South-West of MLBS site).
Real-time reactor power monitoring is critical for a variety of nuclear applications, spanning safety, security, operations, and maintenance. While machine learning methods have shown promise in monitoring reactor power levels, there is limited research on their efficacy in label-starved environments. The goal of this work is to assess the feasibility of classifying nuclear reactor power level using multisource data in scenarios with limited labels. Data were collected using low-resolution multisensors at four nuclear reactor facilities: two large research reactors and two TRIGA reactors. Within each pair, one reactor dataset served as the source and the other as the target in a transfer learning paradigm. Twenty-three supervised models were trained on labeled sequences of magnetic field and acceleration data from each of the target sites. Self-learning and transfer learning methods were applied to the top performing models to assess their classification performance with increasing amounts of labeled data. While reactor power level classification was achieved with a Matthews Correlation Coefficient of up to 0.739 ± 0.003 and 0.622 ± 0.009 with only 400 sequences per power state for the large research reactor and TRIGA target sites, respectively, self-learning and transfer learning leveraging source site data did not improve target classification performance. These findings suggest that alternative methods, such as higher sensitivity sensors, digital twins, or the use of physics-informed models, are required to enable high-performance classification in machine learning approaches to reactor monitoring with a dearth of target ground truth.