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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
NASA NTRS2022

Climate Drivers of Global Wildfire Burned Area

Wildfire is an integral part of the Earth system, but at the same time it can pose serious threats to human society and to certain types of terrestrial ecosystems. Meteorological conditions are a key driver of wildfire activity and extent, which led to the emergence of the use of fire danger indices that depend solely on weather conditions. The Canadian Fire Weather Index (FWI) is a widely used fire danger index of this kind. Here, we evaluate how well the FWI, its components, and the climate variables from which it is derived, correlate with observation-based burned area (BA) for a variety of world regions. We use a novel technique, according to which monthly BA are grouped by size for each Global Fire Emissions Database (GFED) pyrographic region. We find strong correlations of BA anomalies with the FWI anomalies, as well as with the underlying deviations from their climatologies for the four climate variables from which FWI is estimated, namely, temperature, relative humidity, precipitation, and wind. We quantify the relative sensitivity of the observed BA to each of the four climate variables, finding that this relationship strongly depends on the pyrographic region and land type. Our results indicate that the BA anomalies strongly correlate with FWI anomalies at a GFED region scale, compared to the strength of the correlation with individual climate variables. Additionally, among the individual climate variables that comprise the FWI, relative humidity and temperature are the most influential factors that affect the observed BA. Our results support the use of the composite fire danger index FWI, as well as its sub-indices, the Build-Up Index (BUI) and the Initial Spread Index (ISI), comparing to single climate variables, since they are found to correlate better with the observed forest or non-forest BA, for the most regions across the globe.

Wildfire↗
DOE OSTI2026

Online LIBS–ML Framework for Dynamic Characterization of Heterogeneous Waste-Derived Gasification Feedstocks

LIBS−ML framework for real time feedstock characterization during continuous conveyor transport Heterogeneous waste derived feedstocks (e.g., waste coal, biomass and blends) introduce rapid variability in heating value and ash chemistry that affect gasifier operation, yet conventional laboratory characterization techniques are too slow to support proactive control. To address this gap, this study reports on an online, in situ, dynamic characterization framework that couple’s laser-induced breakdown spectroscopy (LIBS) with leakage safe machine learning (ML) regression to deliver real time, decision quality predictions of gasifier relevant properties. A controlled sample matrix spanning two different waste coals, two different biomasses, and engineered blends under two particle size conditions were constructed and benchmarked using standardized laboratory analyses for proximate/ultimate properties and ash composition. LIBS spectra were acquired dynamically as material flowed on a conveyor belt, using high energy 1064 nm laser ablation and shot averaging to improve repeatability and precision. Supervised regression models (multi layer perceptron (MLP) /artificial neural network (ANN), random forest (RF), and support vector regression (SVR)) and an optimized weighted ensemble were trained on emission line feature sets using nested cross validation with Bayesian hyperparameter tuning and validated against an independent hold out set. The proposed LIBS−ML workflow achieves near laboratory predictive fidelity across parametric targets (including higher heating value (HHV), ash content, fixed carbon, sulfur, major ash forming oxides, and initial deformation temperature (IDT)), with the weighted ensemble providing a robust default predictor under dynamic measurement conditions. These results demonstrate a practical pathway for real time feedstock characterization that can enable feedforward adjustments and more resilient gasifier operation for variable quality waste derived fuels.

Biomass↗
DOE OSTI2020

Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm

Abstract We present a benchmark test suite and an automated machine learning procedure for evaluating supervised machine learning (ML) models for predicting properties of inorganic bulk materials. The test suite, Matbench, is a set of 13 ML tasks that range in size from 312 to 132k samples and contain data from 10 density functional theory-derived and experimental sources. Tasks include predicting optical, thermal, electronic, thermodynamic, tensile, and elastic properties given a material’s composition and/or crystal structure. The reference algorithm, Automatminer, is a highly-extensible, fully automated ML pipeline for predicting materials properties from materials primitives (such as composition and crystal structure) without user intervention or hyperparameter tuning. We test Automatminer on the Matbench test suite and compare its predictive power with state-of-the-art crystal graph neural networks and a traditional descriptor-based Random Forest model. We find Automatminer achieves the best performance on 8 of 13 tasks in the benchmark. We also show our test suite is capable of exposing predictive advantages of each algorithm—namely, that crystal graph methods appear to outperform traditional machine learning methods given ~10 4 or greater data points. We encourage evaluating materials ML algorithms on the Matbench benchmark and comparing them against the latest version of Automatminer.

36 MATERIALS SCIENCE↗
NASA NTRS1982

Use of remote sensing for land use policy formulation

Research projects described include: (1) identifying coniferous forest types in Michigan using LANDSAT imagery; (2) investigating synoptic temperature patterns in Michigan as determined via GOES and HCMM thermal imagery; (3) land surface change detection using satellite data and a geographic data base; (4) determining soil map unit composition by electronic scanning densitometry; and (5) delimiting areas of virus infection in vineyards and blueberry fields in southwestern and western Michigan. Contractual activities involve important farmlands inventory, changes in aquatic vegetation in Saginaw Bay, digitized soil association map of Michigan, and aerial photography for hybrid-poplar research. On-going projects are also being conducted in Jamaica, Honduras, the Dominican Republic and Kenya.

Source record↗
DOE OSTI2020

High-yield growth kinetics and spatial mapping of single-walled carbon nanotube forests at wafer scale

Emerging commercial applications of vertically aligned, single-walled carbon nanotube (SWCNT) “forests” require synthesis that minimizes nanotube diameter while maximizing number density across substrate areas exceeding centimeter scale. To address this need, we synthesized SWCNT forests on full silicon wafers with notable reproducibility and uniformity, and co-optimized growth for small diameters and high densities across large areas to access new territory in this 3D parameter space. We mapped the spatial uniformity of key structural features using Raman microscopy, synchrotron X-ray scattering, and Rutherford backscattering spectrometry. Low C 2 H 2 flux over sub-nm Fe/Mo catalysts produced small-diameter SWCNTs (2.1 nm) at high number densities (2.26 × 10 12 cm -2 ) on wafers up to 6 in. Although removing Mo resulted in larger SWCNT diameters and lower densities (<0.7 × 10 12 cm -2 ), mass conversion rates from C 2 H 2 to SWCNT product were high and remarkably invariant for catalyst compositions and densities (i.e., 47.7% or 1.30 × 10 6 % g-catalyst -1 on 4-in. wafers). These carbon conversion efficiencies far exceed typical benchtop reactors and are on par with the best reported literature values. Our detailed elucidation of correlations among structural characteristics within this resource-efficient process is expected to guide future scale-up efforts of SWCNT forest growth beyond wafer scale.

36 MATERIALS SCIENCE↗
NASA NTRS1993

Recent advances in radar remote sensing of forest

On a global scale, forests represent most of the terrestrial standing biomass (80 to 90 percent). Thus, natural and anthropogenic change in forest covers can have major impacts not only on local ecosystems but also on global hydrologic, climatic, and biogeochemical cycles that involve exchange of energy, water, carbon, and other elements between the earth and atmosphere. Quantitative information on the state and dynamics of forest ecosystems and their interactions with the global cycles appear necessary to understand how the earth works as a natural system. The information required includes the lateral and vertical distribution of forest cover, the estimates of standing biomass (woody and foliar volume), the phenological and environmental variations and disturbances (clearcutting, fires, flood), and the longer term variations following deforestation (regeneration, successional stages). To this end, seasonal, annual, and decadal information is necessary in order to separate the long term effects in the global ecosystem from short term seasonal and interannual variations. Optical remote sensing has been used until now to study the forest cover at local, regional, and global scales. Radar remote sensing, which provides recent SAR data from space on a regular basis, represents an unique means of consistently monitoring different time scales, at all latitudes and in any atmospheric conditions. Also, SAR data have shown the potential to detect several forest parameters that cannot be inferred from optical data. The differences--and complementarity--lie in the penetration capabilities of SAR data and their sensitivity to dielectric and geometric properties of the canopy volume, whereas optical data are sensitive to the chemical composition of the external foliar layer of the vegetation canopy.

Letoan, Thuy↗
NASA NTRS2012

Overview of the 2010 Carbonaceous Aerosols and Radiative Effects Study (CARES)

Substantial uncertainties still exist in the scientific understanding of the possible interactions between urban and natural (biogenic) emissions in the production and transformation of atmospheric aerosol and the resulting impact on climate change. The US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program's Carbonaceous Aerosol and Radiative Effects Study (CARES) carried out in June 2010 in Central Valley, California, was a comprehensive effort designed to improve this understanding. The primary objective of the field study was to investigate the evolution of secondary organic and black carbon aerosols and their climate-related properties in the Sacramento urban plume as it was routinely transported into the forested Sierra Nevada foothills area. Urban aerosols and trace gases experienced significant physical and chemical transformations as they mixed with the reactive biogenic hydrocarbons emitted from the forest. Two heavily-instrumented ground sites - one within the Sacramento urban area and another about 40 km to the northeast in the foothills area - were set up to characterize the evolution of meteorological variables, trace gases, aerosol precursors, aerosol size, composition, and climaterelated properties in freshly polluted and "aged" urban air. On selected days, the DOE G-1 aircraft was deployed to make similar measurements upwind and across the evolving Sacramento plume in the morning and again in the afternoon. The NASA B-200 aircraft, carrying remote sensing instruments, was also deployed to characterize the vertical and horizontal distribution of aerosols and aerosol optical properties within and around the plume. This overview provides: (a) the scientific background and motivation for the study, (b) the operational and logistical information pertinent to the execution of the study, (c) an overview of key observations and initial findings from the aircraft and ground-based sampling platforms, and (d) a roadmap of planned data analyses and focused modeling efforts that will facilitate the integration of new knowledge into improved representations of key aerosol processes and properties in climate models.

aerosols↗
DOE OSTI2022

Disturbance legacies regulate coastal forest soil stability to changing salinity and inundation: A soil transplant experiment

Coastal forests worldwide are vulnerable to a dramatic transition from upland to wetland as sea-level rise accelerates and regimes of precipitation and storms change. However, the biogeochemical impacts of shifting salinity and inundation disturbance that foreshadow forest to wetland state transitions are largely unknown. This experiment used a natural salinity gradient in a tidal creek in eastern Maryland, U.S.A., to examine how soil respiration and chemistry may change under novel salinity and inundation disturbance regimes. Soil monoliths were transplanted in a reciprocal design among plots varying in seawater exposure and elevation above the creek. We monitored the monoliths’ carbon dioxide (CO 2 ) flux for two years and performed soil chemical analyses at the end of the experiment. Soil CO 2 flux was affected by changing disturbance regimes and responses were dependent upon the salinity and inundation legacies associated with each study location. Lowland soil CO 2 flux was resistant to changing salinity and inundation disturbance, with transplanted soil monolith chemistry composition in between that of its origin and destination. Conversely, upland soil CO 2 flux was sensitive to changing salinity and inundation disturbance and remained suppressed throughout the 2-year study when exposed to wetter, saline conditions. Additionally, transplanted upland soil chemistry composition was like that of its destination, rather than origin, with upland soils displaying higher pH, base saturation, and nutrient availability relative to lowland soils. Here, we hypothesize that reductions in soil respiration rates were driven by loss of soil nutrients and osmotic and redox stress on microbial communities following exposure to seawater. Together, our results suggest that disturbance legacies shape coastal forest soil responses to changing salinity and inundation disturbance regimes. However, fully understanding the dependence of system responses on disturbance legacies requires future study across a variety of systems and spatial and temporal scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

TEMPEST3 surface runoff water chemistry and organic matter composition

Coastal flooding, driven by storm surges and sea level rise, can mobilize organic matter (OM) via runoff, while introducing compositionally distinct OM (e.g., estuarine OM) into the system. To understand event-scale OM dynamics, we monitored source waters and surface runoff during an ecosystem-scale field manipulation experiment, TEMPEST (Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments), in June 2024. The TEMPEST experiment is part of the COMPASS-FME (Coastal Observations, Mechanisms, and Predictions Across Systems and Scales – Field, Measurements, and Experiments) project and designed to investigate biogeochemical and ecological impacts of freshwater and seawater flooding on coastal terrestrial-aquatic interface ecosystems by simulating freshwater and seawater storm events in two 2000m2 coastal upland forest plots (freshwater and brackish seawater plots). The temporal coverage of this dataset is during the TEMPESTⅢ event (June 11-13, 2024). This dataset contains: - Surface runoff discharge measured by flumes - Sensor data (specific conductivity, salinity, dissolved oxygen, and temperature) - Particle size distribution - Total suspended sediment concentrations (TSS), particulate and dissolved organic carbon (POC, DOC) concentrations, total nitrogen and total dissolved nitrogen (TN, TDN) concentrations - Bulk particulate and dissolved OM compositions (stable C and N isotopes of particulates and optical measurements of chromophoric dissolved OM) - High resolution mass spectrometry analysis data - Water isotope data All data files are plain-text CSV (comma-separated value), and no special software is required to read them.

COMPASS-FME↗
DOE OSTI2021

Divergent species-specific impacts of whole ecosystem warming and elevated CO 2 on vegetation water relations in an ombrotrophic peatland

Boreal peatland forests have relatively low species diversity and thus impacts of climate change on one or more dominant species could shift ecosystem function. Despite abundant soil water availability, shallowly rooted vascular plants within peatlands may not be able to meet foliar demand for water under drought or heat events that increase vapor pressure deficits while reducing near surface water availability, although concurrent increases in atmospheric CO 2 could buffer resultant hydraulic stress. We assessed plant water relations of co-occurring shrub (primarily Rhododendron groenlandicum and Chamaedaphne calyculata) and tree (Picea mariana and Larix laricina) species prior to, and in response to whole ecosystem warming (0 to +9°C) and elevated CO 2 using 12.8-m diameter open-top enclosures installed within an ombrotrophic bog. Water relations (water potential [Ψ], turgor loss point, foliar and root hydraulic conductivity) were assessed prior to treatment initiation, then Ψ and peak sap flow (trees only) assessed after 1 or 2 years of treatments. Under the higher temperature treatments, L. laricina Ψ exceeded its turgor loss point, increased its peak sap flow, and was not able to recover Ψ overnight. In contrast, P. mariana operated below its turgor loss point and maintained constant Ψ and sap flow across warming treatments. Similarly, C. calyculata Ψ stress increased with temperature while R. groenlandicum Ψ remained at pretreatment levels. The more anisohydric behavior of L. laricina and C. calyculata may provide greater net C uptake with warming, while the more conservative P. mariana and R. groenlandicum maintained greater hydraulic safety. These latter species also responded to elevated CO 2 by reduced Ψ stress, which may also help limit hydraulic failure during periods of extreme drought or heat in the future. Along with Sphagnum moss, the species-specific responses of peatland vascular communities to drier or hotter conditions will shape boreal peatland composition and function in the future.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Nature-based climate solutions can help mitigate the radiative forcing that follows deforestation

Widespread expansion of agriculture and forestry has altered the surface of the Earth, the composition of the atmosphere, and as a result, the climate. Here we quantify the radiative forcing caused by the historical deforestation of an ecoregion in the U.S. Upper Midwest and the adoption of eight nature-based climate solutions. We combined regional forest inventory data with over three decades of remote sensing and in situ data from a replicated land use change experiment. Deforestation of the region caused net global warming (1626 ± 44 µW m -2 ), mainly from the 76 % reduction of ecosystem carbon stocks, but also from the 84 % reduction of the soil methane sink and the 115 % increase in soil nitrous oxide emissions. The associated albedo increase offset 24 % of this greenhouse gas induced warming. For the adoption of nature-based climate solutions, we found that conservation agriculture can provide -39 to -76 ± 31 µW m -2 of climate mitigation over a 100-year time period while short/medium length forestry rotations can provide more at -296 to -881 ± 44 µW m -2 and natural forest regeneration can provide the most at -1555 ± 44 µW m -2 . As the impacts of climate change on nature and society intensify, consideration should be given to the climate mitigation, habitat, and ecosystem services that nature-based climate solutions can provide.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1975

Gypsy moth defoliation assessment: Forest defoliation in detectable from satellite imagery

The author has identified the following significant results. ERTS-1 imagery obtained over eastern Pennsylvania during July 1973, indicates that forest defoliation is detectable from satellite imagery and correlates well with aerial visual survey data. It now appears that two damage classes (heavy and moderate-light) and areas of no visible defoliation can be detected and mapped from properly prepared false composite imagery. In areas where maple is the dominant species or in areas of small woodlots interspersed with agricultural areas, detection and subsequent mapping is more difficult.

Moore, H. J.↗
NASA NTRS1997

Improved Passive Microwave Algorithms for North America and Eurasia

Microwave algorithms simplify complex physical processes in order to estimate geophysical parameters such as snow cover and snow depth. The microwave radiances received at the satellite sensor and expressed as brightness temperatures are a composite of contributions from the Earth's surface, the Earth's atmosphere and from space. Owing to the coarse resolution inherent to passive microwave sensors, each pixel value represents a mixture of contributions from different surface types including deep snow, shallow snow, forests and open areas. Algorithms are generated in order to resolve these mixtures. The accuracy of the retrieved information is affected by uncertainties in the assumptions used in the radiative transfer equation (Steffen et al., 1992). One such uncertainty in the Chang et al., (1987) snow algorithm is that the snow grain radius is 0.3 mm for all layers of the snowpack and for all physiographic regions. However, this is not usually the case. The influence of larger grain sizes appears to be of more importance for deeper snowpacks in the interior of Eurasia. Based on this consideration and the effects of forests, a revised SMMR snow algorithm produces more realistic snow mass values. The purpose of this study is to present results of the revised algorithm (referred to for the remainder of this paper as the GSFC 94 snow algorithm) which incorporates differences in both fractional forest cover and snow grain size. Results from the GSFC 94 algorithm will be compared to the original Chang et al. (1987) algorithm and to climatological snow depth data as well.

Foster, James↗
DOE OSTI2023

Metal hydride composition-derived parameters as machine learning features for material design and H 2 storage

Though hydrogen is a promising energy carrier for a green future, many challenges persist. One is the difficulty in engineering storage solutions, with metal hydrides being a leading contender among solid-state strategies. To facilitate efficient searching of candidate materials, ridge regression, simple decision trees, random forest ensembles, and gradient boosting ensembles were employed to predict the energy of formation, with the random forest ensemble resulting in the lowest test set error. First, two public databases, Materials Project and HydPark, were searched for metal hydrides. Feature engineering was performed before the models were developed, resulting in electronegativity, density, atomic density, d-character, f-character, band gap, hydrogen weight fraction, magnetization, temperature, and pressure being retained. The models were then benchmarked by the lowest test error before a random forest ensemble was used to populate entries missing energy of formation. Furthermore, all were then scored by hydrogen storage capacity and energy of formation suitability. Readily available features including several derived from only the chemical formula which were found to be highly predictive. and so are promising for high-throughput screening of arbitrary novel hydride formulations and blends for thermodynamic feasibility.

25 ENERGY STORAGE↗
NASA NTRS2000

BOREAS Forest Cover Data Layers of the NSA in Raster Format

This data set was processed by BORIS staff from the original vector data of species, crown closure, cutting class, and site classification/subtype into raster files. The original polygon data were received from Linnet Graphics, the distributor of data for MNR. In the case of the species layer, the percentages of species composition were removed. This reduced the amount of information contained in the species layer of the gridded product, but it was necessary in order to make the gridded product easier to use. The original maps were produced from 1:15,840-scale aerial photography collected in 1988 over an area of the BOREAS NSA MSA. The data are stored in binary, image format files and they are available from Oak Ridge National Laboratory. The data files are available on a CD-ROM (see document number 20010000884).

Hall, Forrest G.↗
DOE OSTI2023

Vegetation browning: global drivers, impacts, and feedbacks

As global climate conditions continue to change, disturbance regimes and environmental drivers will continue to shift, impacting global vegetation dynamics. Following a period of vegetation greening, there has been a progressive increase in remotely sensed vegetation browning globally. Further, given the many societal benefits that forests provide, it is critical that we understand vegetation dynamic alterations. Here, we review associative drivers, impacts, and feedbacks, revealing the complexity of browning. Concomitant increases in browning include the weakening of ecosystem services and functions and alterations to vegetation structure and species composition as well as the development of potential positive climate change feedbacks. Also discussed are the current challenges in browning detection and understanding associated impacts and feedbacks. Finally, we outline recommended strategies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A neotropical perspective on the uniqueness of the Holocene among interglacials

Abstract Understanding how tropical systems have responded to large-scale climate change, such as glacial-interglacial oscillations, and how human impacts have altered those responses is key to current and future ecology. A sedimentary record recovered from Lake Junín, in the Peruvian Andes (4085 m elevation) spans the last 670,000 years and represents the longest continuous and empirically-dated record of tropical vegetation change to date. Spanning seven glacial-interglacial oscillations, fossil pollen and charcoal recovered from the core showed the general dominance of grasslands, although during the warmest times some Andean forest trees grew above their modern limits near the lake. Fire was very rare until the last 12,000 years, when humans were in the landscape. Here we show that, due to human activity, our present interglacial, the Holocene, has a distinctive vegetation composition and ecological trajectory compared with six previous interglacials. Our data reinforce the view that modern vegetation assemblages of high Andean grasslands and the presence of a defined tree line are aspects of a human-modified landscape.

Science & Technology - Other Topics↗