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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

AmeriFlux FLUXNET-1F US-KFS Kansas Field Station

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-KFS Kansas Field Station. This is the FLUXNET version of the carbon flux data for the site US-KFS Kansas Field Station produced by applying the standard ONEFlux (1F) software. Site Description - The study is an abandoned grassland at the Kansas Field Station and Ecological Reserves. The site is located within the tallgrass prairie-deciduous forest ecotonal area. The site was subjected to intensive agriculture from the 1940s through the late 1960s. In the mid-1970s, the site was planted with the cool-season grass Bromus inermis and used as a hay meadow until 1987. Then, mowing and burning approximately every five years maintained it as a grassland until 2007, when the eddy flux tower was installed.

Brunsell, Nathaniel↗
DOE OSTI2023

AmeriFlux FLUXNET-1F US-Wi5 Mixed young jack pine (MYJP)

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-Wi5 Mixed young jack pine (MYJP). This is the FLUXNET version of the carbon flux data for the site US-Wi5 Mixed young jack pine (MYJP) produced by applying the standard ONEFlux (1F) software. Site Description - The Wisconsin Mixed Young Jack Pine site is located in the Washburn Ranger District of the northeastern section of Chequamegon National Forest. A member of the northern coniferous-deciduous biome, surveys from the mid-19th century indicate the region consisted of a mixed stand of red, white, and jack pines. After extensive timber harvesting, wildfires, and farming activity, the region turned into a fragmented mosaic of stands of various ages and composition. As an assemblage, the ten Wisconsin sites are indicative of the successional stages of development in the predominant stand types of a physically homogeneous landscape. Clearcut on 40 to 70 year intervals, jack pine stands occupy approximately 13% of the region.

Chen, Jiquan↗
DOE OSTI2026

AmeriFlux FLUXNET-1F BR-ITA MataFLUX: Multi-species tree restoration planting

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.

Guillemot, Joannès [Cirad]↗
DOE OSTI2024

AmeriFlux FLUXNET-1F US-PFc NW2 Aspen-1 CHEESEHEAD 2019

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-PFc NW2 Aspen-1 CHEESEHEAD 2019. This is the FLUXNET version of the carbon flux data for the site US-PFc NW2 Aspen-1 CHEESEHEAD 2019 produced by applying the standard ONEFlux (1F) software. Site Description - This tower (12m Rohn) is located in the northwestern quadrant of the 10 x 10km study domain. It is located in a forest of short aspen (3m tall). Several older aspen and conifers are interspersed within the regrowing aspen forest (15-20m tall).

Desai, Ankur↗
DOE OSTI2024

AmeriFlux FLUXNET-1F US-PFL SW2 Aspen-3 CHEESEHEAD 2019

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-PFL SW2 Aspen-3 CHEESEHEAD 2019. This is the FLUXNET version of the carbon flux data for the site US-PFL SW2 Aspen-3 CHEESEHEAD 2019 produced by applying the standard ONEFlux (1F) software. Site Description - This tower (25m Rohn) is located in the southwestern quadrant of the 10 x 10km study domain. It is located in an aspen forest (canopy height: 15 - 19.2 m).

Desai, Ankur↗
DOE OSTI2025

Next-Level Energy Management in Manufacturing: Facility-Level Energy Digital Twin Framework Based on Machine Learning and Automated Data Collection

This research introduces an energy prediction framework at the facility level supported by automated data collection and machine learning models. It investigates whether reducing the prediction time scale allows for applying more complex machine learning techniques and if those techniques improve the prediction accuracy. The primary advantages of this framework lie in its automation of the energy prediction process and its provision of real-time energy data suitable for use in energy dashboards or digital twins. A sitewide dataset was created by combining 15 min energy and daily production data of five shops—assembly, battery, body (electric), body (gas), and paint—from a globally recognized electric vehicle manufacturer. Various machine learning models were evaluated on daily, weekly, and monthly datasets, including, in increasingly complex order: naïve, simple linear regression, net regularized generalized linear regression, principal component regression, k-nearest neighbor, random forest, and Bayesian regularized neural network. Compared to the current state-of-the-art energy consumption prediction for the industrial facility level, this research investigates more complex models and smaller time intervals for higher accuracy. The findings revealed that the more complex monthly models require a minimum of a year and a half of data to operate, while weekly models demand a year of data to achieve improved accuracy. Daily models can operate with only six months of data but exhibit poor performance due to reduced prediction accuracy of production. Key challenges identified include access to reliable, high-quality energy and production data and the initial demand for human labor.

digital twin↗
DOE OSTI2024

Macroscopic Traffic Modeling Using Probe Vehicle Data: A Machine Learning Approach

Abstract The macroscopic fundamental diagram (MFD) captures an orderly relationship among traffic flow, density, and speed at the network level. It is a simple yet powerful tool for modeling traffic dynamics in large urban networks with broad application in traffic control and management. However, empirically derived MFDs in urban regions require high-resolution traffic data from the network. Having the network flow and vehicular density estimated at the (granular) census tract level using vehicle probe data, we apply machine learning methods to predict the MFDs across U.S. urban areas and capture the impacts of location-specific input features on the network flow–density relationships at a large scale. The results show that, among the four tested machine learning approaches (Random Forest, XGBoost, Support Vector Machine, and Neural Network), XGBoost delivers the best performance in predicting network traffic flow based on vehicular density and location attributes. Using interaction Shapley Additive explanation (SHAP) values and partial correlation analysis, we examine the factors influencing MFD shapes across different locations. Our empirical findings reveal that across U.S. urban areas, network topology, transportation infrastructure, and land use are primary factors shaping MFD curves, while demand and trip-related factors play a lesser role. Specifically, higher ranking roads, centrality, and development levels correlate positively with network capacity and critical density, whereas negative associations are observed for network connectivity, mixed-use development, and road roughness levels.

Jin, Ling↗
DOE OSTI2025

AmeriFlux FLUXNET-1F CL-SDP Senda Darwin Peatland

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site CL-SDP Senda Darwin Peatland. This is the FLUXNET version of the carbon flux data for the site CL-SDP Senda Darwin Peatland produced by applying the standard ONEFlux (1F) software. Site Description - Located at the Senda Darwin Biological Station, in a old-growth rainforest. Locally named "pomponal" from the common name of Sphagnum, is an anthropogenic peatland, formed after the forest is cut or a forest fire. The station is 15 km east of Ancud, in the Chiloé Island, close to the Huicha river.

Perez-Quezada, Jorge [University of Chile]↗
DOE OSTI2025

AmeriFlux FLUXNET-1F US-PFg NE1 Pine-2 CHEESEHEAD 2019

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-PFg NE1 Pine-2 CHEESEHEAD 2019. This is the FLUXNET version of the carbon flux data for the site US-PFg NE1 Pine-2 CHEESEHEAD 2019 produced by applying the standard ONEFlux (1F) software. Site Description - This tower (32m, trailer-based, telescoping) is located in the northeastern quadrant of the 10 x 10km study domain. It is located in a forest with red pine (~32m) overstory and maple (10-15m) understory.

Desai, Ankur [University of Wisconsin-Madison]↗
DOE OSTI2025

AmeriFlux FLUXNET-1F US-PFh NE2 Pine-3 CHEESEHEAD 2019

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-PFh NE2 Pine-3 CHEESEHEAD 2019. This is the FLUXNET version of the carbon flux data for the site US-PFh NE2 Pine-3 CHEESEHEAD 2019 produced by applying the standard ONEFlux (1F) software. Site Description - This tower (32m, trailer-based, telescoping) is located in the northeastern quadrant of the 10 x 10km study domain. It is located in a forest jack pine forest (18 - 20m), with some aspens.

Desai, Ankur [University of Wisconsin-Madison]↗
DOE OSTI2023

Natural attenuation of uranium in a fluvial Wetland: Importance of hydrology and speciation

A nuclear fuel fabrication facility released 43,500 kg of uranium into a riparian wetland located on the Savannah River Site between 1955 and 1988. Studies were undertaken to evaluate hydrological and geochemical processes influencing uranium accumulation in the wetland. Gamma-radiation-mapping surveys were conducted by systematically walking over the contaminated wetland with backpacks equipped with global positioning systems and NaI gamma detectors. Based on maps compiled from >700,000 gamma spectra and eight sediment uranium depth profiles, it was determined that 94% of the released uranium remained in the wetland. The uranium in the wetland is concentrated in five multi-hectare areas along the stream, accounting for ~11% of the land area adjacent to the stream. While land type (upland or wetland) and topography provided a reasonable first approximation of where much of the uranium was deposited, hydrological watershed modeling revealed that the stream velocity was especially slow through many of the hot spots. Here, using autoradiography combined with SEM/EDX measurements of contaminated sediments, surprisingly few hot particles were detected. Instead, uranium was evenly distributed throughout the sampled sediment, suggesting that dissolved uranium had bound to sediment particles that became suspended and later deposited in low energy (low flow velocity) portions of the stream. EXAFS suggested that U atoms were present as individual ions in disordered complexes within the sediment. Furthermore, linear combination analyses suggested that the predominant component of the U(VI) was adsorbed to sediment minerals (~70%) and a minor component (~30%) was associated with organic matter phases. Furthermore, these studies show that wetlands can be extraordinarily effective at binding and retaining uranium, thereby providing a natural barrier to the transport of uranium out of a watershed. However, significant anthropogenic or climatic changes to wetlands, such as those associated with flooding, forest fires, or land use, may disrupt the complex hydrological and biogeochemical balance necessary to maintain long-term immobilization of uranium.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Spatiotemporal dynamics of ecosystem fires and biomass burning-induced carbon emissions in China over the past two decades

Fire is a major type of disturbance that has important influences on ecosystem dynamics and carbon cycles. Yet our understanding of ecosystem fires and their carbon cycle consequences is still limited, largely due to the difficulty of large-scale fire monitoring and the complex interactions between fire, vegetation, climate, and anthropogenic factors. Here, using data from satellite-derived fire observations and ecosystem model simulations, we performed a comprehensive investigation of the spatial and temporal dynamics of China's ecosystem fire disturbances and their carbon emissions over the past two decades (1997–2016). Satellite-derived results showed that on average about 3.47 - 4.53 x 10 4 km 2 of the land was burned annually during the past two decades, among which annual burned forest area was about 0.81 - 1.25 × 10 4 km 2 , accounting for 0.33-0.51% of the forest area in China. Biomass burning emitted about 23.02 TgC per year. Compared to satellite products, simulations from the Energy Exascale Earth System Land Model (ELM) strongly overestimated China's burned area and fire-induced carbon emissions. Annual burned area and fire-induced carbon emissions were high for boreal forest in Northeast China's Daxing'anling region and subtropical dry forest in South Yunnan, as revealed by both the satellite product and the model simulations. Our results suggest that climate and anthropogenic factors play critical roles in controlling the spatial and seasonal distribution of China's ecosystem fire disturbances. Our findings highlight the importance of multiple complementary approaches in assessing ecosystem fire disturbance and its carbon consequences. Further studies are required to improve the methods of observing and modelling China's ecosystem fire disturbances, which will provide valuable information for fire management and ecosystem sustainability in an era when both human activities and the natural environment are rapidly changing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

AmeriFlux FLUXNET-1F US-BRG Bayles Road Grassland Tower

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-BRG Bayles Road Grassland Tower. This is the FLUXNET version of the carbon flux data for the site US-BRG Bayles Road Grassland Tower produced by applying the standard ONEFlux (1F) software. Site Description - The Bayles Road grassland tower is situated in an experimental field incorporated within the Indiana University Research and Teaching Preserve. The field was historically covered by bottomland hardwood forest and later maintained for plant genetics studies. Intermixed species of grasses, sedges, and forbs are currently maintained with occasional mowing.

Novick, Kimberly↗
DOE OSTI2024

AmeriFlux FLUXNET-1F US-PFb NW1 Pine-1 CHEESEHEAD 2019

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-PFb NW1 Pine-1 CHEESEHEAD 2019. This is the FLUXNET version of the carbon flux data for the site US-PFb NW1 Pine-1 CHEESEHEAD 2019 produced by applying the standard ONEFlux (1F) software. Site Description - This tower (32m, trailer-based, telescoping) is the northwestern most tower in the 10 x 10km study domain. It is located in a red pine forest (canopy height ~25m). There is a regrowing/trap layer consisting of broadleaf aspen and other deciduous vegetation.

Desai, Ankur↗
DOE OSTI2024

AmeriFlux FLUXNET-1F US-PFk SW1 Aspen-2 CHEESEHEAD 2019

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-PFk SW1 Aspen-2 CHEESEHEAD 2019. This is the FLUXNET version of the carbon flux data for the site US-PFk SW1 Aspen-2 CHEESEHEAD 2019 produced by applying the standard ONEFlux (1F) software. Site Description - This tower (32m, trailer-based, telescoping) is located in the southwestern quadrant of the 10 x 10km study domain. It is located in an aspen forest (canopy height: immediate vicinity - 10m; greater area - 24.4 m).

Desai, Ankur↗
DOE OSTI2024

AmeriFlux FLUXNET-1F US-PFq SE3 Aspen-4 CHEESEHEAD 2019

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-PFq SE3 Aspen-4 CHEESEHEAD 2019. This is the FLUXNET version of the carbon flux data for the site US-PFq SE3 Aspen-4 CHEESEHEAD 2019 produced by applying the standard ONEFlux (1F) software. Site Description - This tower (32m, trailer-based, telescoping) is located in the southeastern quadrant of the 10 x 10km study domain. It is located in an aspen forest (canopy height: 10 - 14.3 m).

Desai, Ankur↗
DOE OSTI2025

AmeriFlux FLUXNET-1F US-PFj NE4 Maple-1 CHEESEHEAD 2019

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-PFj NE4 Maple-1 CHEESEHEAD 2019. This is the FLUXNET version of the carbon flux data for the site US-PFj NE4 Maple-1 CHEESEHEAD 2019 produced by applying the standard ONEFlux (1F) software. Site Description - This tower (32m, trailer-based, telescoping) is located in the northeastern quadrant of the 10 x 10km study domain. It is located in a maple forest (canopy height: 18 - 20m). Some conifers mixed in, more conifers to the NNW.

Desai, Ankur [University of Wisconsin-Madison]↗
DOE OSTI2022

Whole-tree dormant season nitrogen pools for different species receiving combinations of fertilization and irrigation after one short rotation

Our understanding of the accretion and distribution of tree tissue nitrogen (N) pools across a variety of species and genotypes suitable for short-rotation woody crop (SRWC) production in response to water and N availability remains limited. We measured dormant-season, rotation-length, whole-tree N pools for five tree genotypes from four species (two eastern cottonwood, Populus deltoides Bartr., genotypes; American sycamore, Platanus occidentalis L.; American sweetgum, Liquidambar styraciflua L.; and loblolly pine, Pinus taeda L.) receiving irrigation (I), fertilization (F), their combination (IF), or no resources manipulation (C). Our results demonstrate that foliar nitrogen concentration [N] responded to fertilization but was constrained within genotype-specific ranges and varied temporally. Tree genotypes differed in their composite and component tissue N content (N C ), and these differences mostly reflected tissue mass (i.e., larger components and trees resulted in higher N C ). Resource amendments (I, F, IF) resulted in up to 3.8-fold increases in N C compared with C, which were most pronounced for sycamore and sweetgum with F and IF treatments, respectively. By the end of the rotation, forest stands accumulated 73 to 452 kg N ha –1 in tree tissues with 40 – 78 % distributed aboveground and 22 – 60 % distributed belowground. A critical difference between genotypes was that all hardwoods exhibited larger belowground N distributions than the evergreen conifer. Our results stress the importance of belowground N pools and highlight differences among genotypes. Furthermore, our study underscores valuable information about N pools across genotypes suitable for SRWC production, which can be leveraged to inform fertilization plans and devise sustainable nutrient management as production expands across marginal lands.

54 ENVIRONMENTAL SCIENCES↗