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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Creative-Dynamics Approach To Neural Intelligence

Paper discusses approach to mathematical modeling of artificial neural networks exhibiting complicated behaviors reminiscent of creativity and intelligence of biological neural networks. Neural network treated as non-Lipschitzian dynamical system - as described in "Non-Lipschitzian Dynamics For Modeling Neural Networks" (NPO-17814). System serves as tool for modeling of temporal-pattern memories and recognition of complicated spatial patterns.

Zak, Michail A.↗
NASA NTRS2018

A Method for Mapping Future Urbanization in the United States

Cities are poised to absorb additional people. Their sustainability, or ability to accommodate a population increase without depleting resources or compromising future growth, depends on whether they harness the efficiency gains from urban land management. Population is often projected as a bulk national number without details about spatial distribution. We use Landsat and population data in a methodology to project and map U.S. urbanization for the year 2020 and document its spatial pattern. This methodology is important to spatially disaggregate projected population and assist land managers to monitor land use, assess infrastructure and distribute resources. We found the U.S. west coast urban areas to have the fastest population growth with relatively small land consumption resulting in future decrease in per capita land use. Except for Miami (FL), most other U.S. large urban areas, especially in the Midwest, are growing spatially faster than their population and inadvertently consuming land needed for ecosystem services. In large cities, such as New York, Chicago, Houston and Miami, land development is expected more in suburban zones than urban cores. In contrast, in Los Angeles land development within the city core is greater than in its suburbs.

Bounoua, Lahouari↗
NASA NTRS2012

The Role of Forcing and Internal Dynamics in explaining the 'Medieval Climate Anomaly'

Proxy reconstructions suggest that peak global temperature during the past warm interval known as the Medieval Climate Anomaly (MCA, roughly 950-1250 AD) has been exceeded only during the most recent decades. To better understand the origin of this warm period, we use model simulations constrained by data assimilation establishing the spatial pattern of temperature changes that is most consistent with forcing estimates, model physics and the empirical information contained in paleoclimate proxy records. These numerical experiments demonstrate that the reconstructed spatial temperature pattern of the MCA can be explained by a simple thermodynamical response of the climate system to relatively weak changes in radiative forcing combined with a modification of the atmospheric circulation, displaying some similarities with the positive phase of the so-called Arctic Oscillation, and with northward shifts in the position of the Gulf Stream and Kuroshio currents. The mechanisms underlying the MCA are thus quite different from anthropogenic mechanisms responsible for modern global warming.

Goossee, Hugues↗
NASA NTRS2007

Three-dimensional Model of Tissue and Heavy Ions Effects

A three-dimensional tissue model was incorporated into a new Monte Carlo algorithm that simulates passage of heavy ions in a tissue box . The tissue box was given as a realistic model of tissue based on confocal microscopy images. The action of heavy ions on the cellular matrix for 2- or 3-dimensional cases was simulated. Cells were modeled as a cell culture monolayer in one example, where the data were taken directly from microscopy (2-d cell matrix), and as a multi-layer obtained from confocal microscopy (3-d case). Image segmentation was used to identify cells with precise areas/volumes in an irradiated cell culture monolayer, and slices of tissue with many cell layers. The cells were then inserted into the model box of the simulated physical space pixel by pixel. In the case of modeled tissues (3-d), the tissue box had periodic boundary conditions imposed, which extrapolates the technique to macroscopic volumes of tissue. For the real tissue (3-d), specific spatial patterns for cell apoptosis and necrosis are expected. The cell patterns were modeled based on action cross sections for apoptosis and necrosis estimated from current experimental data. A spatial correlation function indicating a higher spatial concentration of damaged cells from heavy ions relative to the low-LET radiation cell damage pattern is presented. The spatial correlation effects among necrotic cells can help studying microlesions in organs, and probable effects of directionality of heavy ion radiation on epithelium and endothelium.

Ponomarev, Artem L.↗
NASA NTRS2022

Using Machine Learning to Identify Novel Hydroclimate States

Anthropogenic climate change is expected to alter drought risk in the future. However, droughts are not uncommon or unprecedented, as documented in tree-ring-based reconstructions of the summer average Palmer drought severity index (PDSI). Using an unsupervised machine-learning method trained on these reconstructions of pre-industrial climate, we identify outliers: years in which the spatial pattern of PDSI is unusual relative to ‘normal' variability. We show that in many regions, outliers are more frequently identified in the twentieth and twenty-first centuries. This trend is more pronounced when the regional drought atlases are combined into a single global dataset. By definition, outlier patterns at the 10% level are expected to occur once per decade, but from 1950 to 2000 more than 6 years per decade are identified as outliers in the global drought atlas (GDA). Extending the GDA through 2020 using an observational dataset suggests that anomalous global drought conditions are present in 80% of years in the twenty-first century. Our results indicate, without recourse to climate models, that the world is more frequently experiencing drought conditions that are highly unusual in the context of past natural climate variability.

Drought risk↗
DOE OSTI2026

A spatiotemporally explicit and scalable indicator of intact lands across the conterminous United States, 1986–2023

Globally, ecologically intact areas are increasingly scarce. Agricultural expansion into previously uncultivated areas drives the loss of intact lands that might otherwise exhibit high levels of ecological integrity. Thus, the absence of cultivation can be an indicator of intact lands as measured from remote sensing data and thematic maps. Our objective for this study was to develop and compare tractable approaches based on remotely sensed satellite data to map spatial patterns of potentially intact lands across the conterminous U.S. (CONUS). Using annual cultivation probabilities derived from satellite observations, we classified and mapped potentially intact lands across CONUS from 1986 to 2023 at 30 m resolution. We created three maps, first by applying a constant cultivation probability threshold across CONUS, second by varying the threshold state-by-state to maximize state-level overall accuracies, and third by equalizing the state-level user's and producer's accuracies to minimize classification bias. Validation against 800,000+ independent ground samples resulted in CONUS-level overall accuracies ≥85% for the roughly 660 million ha of potentially intact land. Map accuracy varied with the proportion of potentially intact lands across regions, with the Pacific-Mountain and Great Plains regions exhibiting the highest accuracies, while Eastern CONUS exhibited a greater mix of potentially intact and non-intact lands and more moderate map accuracies. These novel maps and approaches can be adapted to different spatiotemporal extents to support conservation and production decisions ranging from species and ecosystems protection to reducing land conversion and climate mitigation.

agriculture↗
DOE OSTI2024

Evaluating the effects of heatwave events on hydrological processes in the contiguous United States (2003–2022)

Extreme heat and drought conditions are affecting water availability in many regions worldwide, leading to negative impacts on human societies, agriculture, and ecosystems. However, current research lacks comprehensive spatiotemporal analysis examining the interplay between multiple hydrological factors and heatwave events, especially in the context of climate change. This research broadly pertains to understanding the dynamics of hydrological factors and their potential responses to heatwave during warm seasons across the contiguous United States for the period from 2003 to 2022. Utilizing data from the Global Land Data Assimilation System (GLDAS), we analyzed surface runoff, evapotranspiration (ET), precipitation, Groundwater Storage (GWS), Root Zone Soil Moisture (RZSM), and Total Water Storage (TWS) to discern annual patterns and the impacts of heatwave. Further, the spatial patterns of heatwave highlighted a higher occurrence in the western, central, and northeastern U.S., with longer average durations in the western and south-central regions. These events are predominantly dry, characterized by low Relative Humidity (RH), except in the southeastern U.S., where heatwave coincide with high RH levels. Post-heatwave analysis indicated a reduction in GWS, TWS, RZSM, and ET, alongside an increase in surface runoff, RH, and precipitation. An in-depth examination of rainfall and temperature dynamics during heatwave revealed weak correlations between rainfall and temperature, as well as between rainfall and heatwave duration, highlighting the complex nature of these interactions. The study also found an enhanced probability of rainfall following heatwave, particularly in the eastern regions, drawing attention to the potential for increased flood risks post-heatwave. Our findings contribute to the growing body of knowledge on the impacts of heatwave on hydrological factors, providing valuable insights for climate change adaptation and water resource management strategies.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2002

Phase Synchronization and Desynchronization of Structural Response Induced by Turbulent and External Sound

Acoustic and turbulent boundary layer flow loadings over a flexible structure are used to study the spatial-temporal dynamics of the response of the structure. The stability of the spatial synchronization and desynchronization by an active external force is investigated with an array of coupled transducers on the structure. In the synchronous state, the structural phase is locked, which leads to the formation of spatial patterns while the amplitude peaks exhibit chaotic behaviors. Large amplitude, spatially symmetric loading is superimposed on broadband, but in the desynchronized state, the spectrum broadens and the phase space is lost. The resulting pattern bears a striking resemblance to phase turbulence. The transition is achieved by using a low power external actuator to trigger broadband behaviors from the knowledge of the external acoustic load inducing synchronization. The changes are made favorably and efficiently to alter the frequency distribution of power, not the total power level. Before synchronization effects are seen, the panel response to the turbulent boundary layer loading is discontinuously spatio-temporally correlated. The stability develops from different competing wavelengths; the spatial scale is significantly shorter than when forced with the superimposed external sound. When the external sound level decreases and the synchronized phases are lost, changes in the character of the spectra can be linked to the occurrence of spatial phase transition. These changes can develop broadband response. Synchronized responses of fuselage structure panels have been observed in subsonic and supersonic aircraft; results from two flights tests are discussed.

Maestrello, Lucio↗
NASA NTRS2003

Spatial and Temporal Patterns of Tidal Dissipation in Synchronous Satellites

Tidal heating is an important energy source for several solar system bodies, and there is a wide-spread perception that the pattern of surface heat flow is diagnostic of internal structure. We wish to clarify that situation. Our analysis depends upon two important assumptions: First, that heat transport is dominated by conduction. Second, that the body can be modeled by a sequence of spherically symmetric layers, each with a linear visco-elastic rheology. Under these assumptions, surface heat flow patterns in tidally dominated satellites will reflect radially integrated dissipation patterns. For synchronously rotating satellites with zero obliquity, this pattern depends quite strongly on orbital eccentricity but relatively little on purely radial variations in internal structure. The total amount of heat generated within the body does depend sensitively on internal structure, but the spatial pattern is rather insensitive to structure, especially at low orbital eccentricities.

Bills, Bruce G.↗
NASA NTRS1991

Automated pattern eduction from turbulent flow diagnostics

The development of an automated technique for the eduction of 3-D spatial patterns in vector or scalar diagnostics was completed. The method is based on an iterative convolution between a trial pattern and the data field. It was applied to the analysis of low Reynolds number turbulent channel flow and homogeneous shear flow. The results yielded new information on the dominant flow structures in these flows, particularly with respect to the spatial relationships between various forms of organized motion. A particular application of the pattern eduction method, which is tentatively referred to as an 'adaptive wavelet transformation', is proposed with the objective of investigating the way turbulence structure changes with scale. Preliminary results using data from homogeneous turbulent shear flow simulations are presented. At the low Reynolds numbers of the simulations, there is no evidence of scale similarity. The small scales appear to be associated with the edges of the larger scale vortical structures.

Stretch, Derek D.↗
DOE OSTI2025

End-to-End Automated Segmentation Framework for Four-Dimensional Scanning Transmission Electron Microscopy Data

Four-dimensional scanning transmission electron microscopy (4D-STEM) is powerful for rapidly characterizing arrays of nanoparticles produced via high-throughput synthesis. However, such 4D-STEM datasets typically contain thousands of nanoparticles, each characterized by thousands of diffraction patterns spatially distributed across the nanoparticle, necessitating efficient and comprehensive analysis. We propose an end-to-end segmentation framework to automatically segment each nanoparticle into regions with distinct composition/orientation of crystal grains, using only the 4D-STEM data. Bragg disk information is extracted in a physics-informed manner from the diffraction patterns at each spatial location and combined with the real space coordinates to form feature vectors. These feature vectors are then used as inputs to a Gaussian mixture model (GMM) to segment the nanoparticle into distinct regions. We also develop two visualization tools based on the GMM outputs to infer the interface transition and the degree of superposition. Our framework comprehensively integrates machine learning tools and physics knowledge, and provides a basis for substantially compressing enormous 4D-STEM datasets, e.g., by replacing the full 4D-STEM dataset for each nanoparticle with only a single set of Bragg disk features for each distinct crystal grain identified in the nanoparticle. In this article, we demonstrate the power of our framework by presenting results for real, complex datasets.

47 OTHER INSTRUMENTATION↗
NASA NTRS2015

The Pattern Across the Continental United States of Evapotranspiration Variability Associated with Water Availability

The spatial pattern across the continental United States of the interannual variance of warm season water-dependent evapotranspiration, a pattern of relevance to land-atmosphere feedback, cannot be measured directly. Alternative and indirect approaches to estimating the pattern, however, do exist, and given the uncertainty of each, we use several such approaches here. We first quantify the water dependent evapotranspiration variance pattern inherent in two derived evapotranspiration datasets available from the literature. We then search for the pattern in proxy geophysical variables (air temperature, stream flow, and NDVI) known to have strong ties to evapotranspiration. The variances inherent in all of the different (and mostly independent) data sources show some differences but are generally strongly consistent they all show a large variance signal down the center of the U.S., with lower variances toward the east and (for the most part) toward the west. The robustness of the pattern across the datasets suggests that it indeed represents the pattern operating in nature. Using Budykos hydroclimatic framework, we show that the pattern can largely be explained by the relative strength of water and energy controls on evapotranspiration across the continent.

Air Temperature↗
NASA NTRS2022

Tracking the Impacts of Precipitation Phase Changes Through the Hydrologic Cycle in Snowy Regions: From Precipitation to Reservoir Storage

Cool season precipitation plays a critical role in regional water resource management in the western United States. Throughout the twenty-first century, regional precipitation will be impacted by rising temperatures and changing circulation patterns. Changes to precipitation magnitude remain challenging to project; however, precipitation phase is largely dependent on temperature, and temperature predictions from global climate models are generally in agreement. To understand the implications of this dependence, we investigate projected patterns in changing precipitation phase for mountain areas of the western United States over the twenty-first century and how shifts from snow to rain may impact runoff. We downscale two bias-corrected global climate models for historical and end-century decades with the Weather Research and Forecasting (WRF) regional climate model to estimate precipitation phase and spatial patterns at high spatial resolution (9 km). For future decades, we use the RCP 8.5 scenario, which may be considered a very high baseline emissions scenario to quantify snow season differences over major mountain chains in the western U.S. Under this scenario, the average annual snowfall fraction over the Sierra Nevada decreases by >45% by the end of the century. In contrast, for the colder Rocky Mountains, the snowfall fraction decreases by 29%. Streamflow peaks in basins draining the Sierra Nevada are projected to arrive nearly a month earlier by the end of the century. By coupling WRF with a water resources model, we estimate that California reservoirs will shift towards earlier maximum storage by 1–2 months, suggesting that water management strategies will need to adapt to changes in streamflow magnitude and timing.

Melissa L. Wrzesien↗
DOE OSTI2026

Meta-analysis of North American Arctic and boreal aboveground biomass datasets: assessing accuracy, dynamics, and similarities

The North American arctic and boreal regions (ABRs) are rapidly warming and experiencing intensifying disturbances. Accurately quantifying aboveground biomass (AGB) is critical for understanding the impacts of these changes on the carbon cycle and for designing climate change mitigation strategies. Several AGB maps have been developed for the North American ABRs, including recent contributions from National Aeronautics and Space Administration’s Arctic-Boreal Vulnerability Experiment (ABoVE) campaign. However, these maps differ widely in training data, methodology, and resulting AGB density estimates. Presently, a comprehensive comparative evaluation is lacking, making it difficult for users to select datasets suited to their research or management needs. Here, in this study, we conducted a comparative analysis of nine AGB density datasets across North American ABRs, specifically for Alaska and Canada. We (1) summarized AGB by ecoregion and Canadian provinces, (2) evaluated their accuracy against field-based measurements, (3) analyzed spatial and temporal similarities among datasets, and (4) assessed their ability to capture disturbance (fire and harvest) impacts on AGB. We found substantial variation in regional and local AGB estimates across datasets, with overall accuracy ranging from R 2 = 0.25–0.62 and Bias% from −47.8% to 69.9% when validated against field plots. Despite these differences, most datasets have comparatively consistent spatial patterns in AGB (r > 0.8 for most cases). In contrast, agreement on the temporal patterns of AGB change is generally low. We found datasets with spatial resolutions ⩽300 m are capable of capturing disturbance impacts on AGB dynamics, though sensitivity varies across products. Our findings and dataset summary provide guidance for selecting appropriate AGB datasets for different applications within our study area. Our analysis also highlights the need to decrease map bias and increase capability to detect temporal change to decrease uncertainty of AGB datasets potentially by using training data which is representative of major plant functional types within the mapped area.

ABoVE↗
NASA NTRS1995

Monthly mean large-scale analyses of upper-tropospheric humidity and wind field divergence derived from three geostationary satellites

This paper describes the results from a collaborative study between the European Space Operations Center, the European Organization for the Exploitation of Meteorological Satellites, the National Oceanic and Atmospheric Administration, and the Cooperative Institute for Meteorological Satellite Studies investigating the relationship between satellite-derived monthly mean fields of wind and humidity in the upper troposphere for March 1994. Three geostationary meteorological satellites GOES-7, Meteosat-3, and Meteosat-5 are used to cover an area from roughly 160 deg W to 50 deg E. The wind fields are derived from tracking features in successive images of upper-tropospheric water vapor (WV) as depicted in the 6.5-micron absorption band. The upper-tropospheric relative humidity (UTH) is inferred from measured water vapor radiances with a physical retrieval scheme based on radiative forward calculations. Quantitative information on large-scale circulation patterns in the upper-troposphere is possible with the dense spatial coverage of the WV wind vectors. The monthly mean wind field is used to estimate the large-scale divergence; values range between about-5 x 10(exp -6) and 5 x 10(exp 6)/s when averaged over a scale length of about 1000-2000 km. The spatial patterns of the UTH field and the divergence of the wind field closely resemble one another, suggesting that UTH patterns are principally determined by the large-scale circulation. Since the upper-tropospheric humidity absorbs upwelling radiation from lower-tropospheric levels and therefore contributes significantly to the atmospheric greenhouse effect, this work implies that studies on the climate relevance of water vapor should include three-dimensional modeling of the atmospheric dynamics. The fields of UTH and WV winds are useful parameters for a climate-monitoring system based on satellite data. The results from this 1-month analysis suggest the desirability of further GOES and Meteosat studies to characterize the changes in the upper-tropospheric moisture sources and sinks over the past decade.

Schmetz, Johannes↗
DOE OSTI2024

Hierarchical Composites Patterned via 3D Printed Cellular Fluidics

Additive manufacturing of freeform structures containing multiple materials with deterministic spatial arrangement and interactions remains a challenge for most 3D printing processes, due to complex fabrication tool requirements and limitations in printability of some material classes. Here, in this paper, a versatile method is reported to produce architected composites using the concept of cellular fluidics, in which lattices of unit cells are used as templating scaffolds to guide flowable infill materials in a programmed spatial pattern, upon which they are cured in place to produce a deterministically ordered multimaterial solid. The lattice design relies on the unit cell size, type, strut diameter, surface wetting, and distribution of cellular structures to control liquid flow and retention. Individual unit cells are tuned to achieve reliable infilling and combined into higher-order architectures to achieve multiscale composite materials with disparate mechanical properties, including those considered non-printable. Lattice design considerations for leveraging capillary phenomena and demonstrate several methods of patterning polymers in 3D-printed cellular fluidic structures are presented. The concept of tuning the compressive response of an architected composite using a flexible-elastomer as the lattice and a stiff-epoxy as the infill material is illustrated.

36 MATERIALS SCIENCE↗
NASA NTRS1973

Terrain type recognition using ERTS-1 MSS images

For the automatic recognition of earth resources from ERTS-1 digital tapes, both multispectral and spatial pattern recognition techniques are important. Recognition of terrain types is based on spatial signatures that become evident by processing small portions of an image through selected algorithms. An investigation of spatial signatures that are applicable to ERTS-1 MSS images is described. Artifacts in the spatial signatures seem to be related to the multispectral scanner. A method for suppressing such artifacts is presented. Finally, results of terrain type recognition for one ERTS-1 image are presented.

Gramenopoulos, N.↗