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At least 145 records · Page 8

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NASA NTRS2000

Bioimpedance Measurement of Segmental Fluid Volumes and Hemodynamics

Bioimpedance has become a useful tool to measure changes in body fluid compartment volumes. An Electrical Impedance Spectroscopic (EIS) system is described that extends the capabilities of conventional fixed frequency impedance plethysmographic (IPG) methods to allow examination of the redistribution of fluids between the intracellular and extracellular compartments of body segments. The combination of EIS and IPG techniques was evaluated in the human calf, thigh, and torso segments of eight healthy men during 90 minutes of six degree head-down tilt (HDT). After 90 minutes HDT the calf and thigh segments significantly (P < 0.05) lost conductive volume (eight and four percent, respectively) while the torso significantly (P < 0.05) gained volume (approximately three percent). Hemodynamic responses calculated from pulsatile IPG data also showed a segmental pattern consistent with vascular fluid loss from the lower extremities and vascular engorgement in the torso. Lumped-parameter equivalent circuit analyses of EIS data for the calf and thigh indicated that the overall volume decreases in these segments arose from reduced extracellular volume that was not completely balanced by increased intracellular volume. The combined use of IPG and EIS techniques enables noninvasive tracking of multi-segment volumetric and hemodynamic responses to environmental and physiological stresses.

Montgomery, Leslie D.↗

NASA NTRS2001

Low Temperature Growth of Nanostructured Diamond Films on Metals

The field of nanocrystalline diamond and tetrahedral amorphous carbon films has been the focus of intense experimental activity in the last few years for applications in field emission display devices, optical windows, and tribological coatings, The choice of substrate used in most studies has typically been silicon. For metals, however, the thermal expansion mismatch between the diamond film and substrate gives rise to thermal stress that often results in delamination of the film. To avoid this problem in conventional CVD deposition low substrate temperatures (less than 700 C) have been used, often with the incorporation of oxygen or carbon monoxide to the feedgas mixture. Conventionally grown CVD diamond films are also rough and would require post-deposition polishing for most applications. Therefore, there is an obvious need to develop techniques for deposition of well-adhered, smooth nano-structured diamond films on metals for various tribological applications. In our work, nanostructured diamond films are grown on a titanium alloy substrate using a two-step deposition process. The first step is performed at elevated temperature (820 C) for 30 minutes using a H2/CH4/N2 gas mixture in order to grow a thin (approx. 600 nm) nanostructured diamond layer and improve film adhesion. The remainder of the deposition involves growth at low temperature (less than 600 C) in a H2/CH4/O2 gas mixture. Laser reflectance Interferometry (LRI) pattern during growth of a nanostructured diamond film on Ti-6Al-4V alloy. The first 30 minutes are at a high temperature of 820 C and the rest of the film is grown at a low temperature of 580 T. The fringe pattern is observed till the very end due to extremely low surface roughness of 40 nm. The continuation of the smooth nanostructured diamond film growth during low temperature deposition is confirmed by in-situ laser reflectance interferometry and by post-deposition micro-Raman spectroscopy and surface profilometry. Similar experiments performed without the starting nanostructured diamond layer resulted in poorly adhered films with a more crystalline appearance and a higher surface roughness. This low temperature deposition of nanostructured diamond films on metals offers advantages in cases where high residual thermal stress leads to delamination at high temperatures.

Baker, Paul A.↗

NASA NTRS2019

Indoor Ground Testing of a Small UAS Sense and Avoid Airborne Doppler Radar

The National Aeronautics and Space Administration’s Unmanned Aircraft System (UAS) Traffic Management (UTM) project is researching prototype technologies needed to ensure safe integration of UAS operations into the National Airspace System (NAS). Within the UTM Concept of Operations, UAS would be equipped with on-board Sense and Avoid (SAA) technology to continually monitor for manned and unmanned aircraft in its vicinity while operating beyond visual line of sight in uncontrolled airspace. To support this effort, a candidate commercially available 24.5 GHz Doppler radar was selected and evaluated to determine if the technology could reliably support minimum requirements for SAA applications of small UAS (sUAS). Indoor ground tests were conducted inside the NASA Langley Research Center’s Experimental Test Range (ETR) from a stationary platform to evaluate the Doppler radar performance characteristics and gain operational proficiency before the radar was authorized to transmit outdoors. A high speed linear rail system was developed for the radar evaluation and was shown to be an effective method to generate Doppler radar targets of known radar cross section. The accuracy of the range and velocity reported by the radar was shown to be dependent on the Kalman filter state variance parameter settings. Antenna measurements were collected with the radar installed both on and off a sUAS to quantify the relative antenna gain, beam width and side lobe levels of the radar’s Metamaterial Electronically Scanning Array (MESA) antennas at boresight and extreme field of view pointing vectors. The relative antenna gain measured 2.6 dB lower at extreme field view angles compared to the boresight radiation pattern.

Szatkowski, George N.↗

NASA NTRSDate not supplied

Generation of Continental Scale Percent Tree Cover Product Using Deep-learning and Multi-scale Remote Sensing Data

Spatially explicit percent tree cover (TC) estimation is critical for mapping forest aboveground biomass and its dynamics. While various TC products have been developed, there has not been a generalized framework that can be applied to diverse terrestrial ecosystems due to underlain extreme complexities. Deep learning algorithms can learn a spatial pattern and radiometric characteristics of tree canopy as a robust approximation of physical or empirical models, and thus have emerged as promising and efficient tools for large-scale TC mapping. In this study, we synergistically use very high-resolution aerial imageries (National Agriculture Imagery Program, NAIP) and medium resolution Landsat data to map continental-scale TC (CONUS and Mexico) through a hierarchical deep learning approach (Convolutional Neural Network), i.e., NAIP TC generated from a NAIP model is utilized to train a Landsat model. The produced TC product (hereafter, NEX-TC) is able to capture the spatial pattern of TC distribution and its changes driven by natural disturbance and human land management. We further explore and analyze the reliability and potential uncertainty of the NEX-TC by comparing it to lidar- (lidar-TC), National Land Cover Database (NLCD-TC), and MODIS Vegetation Continuous Field (MODIS-TC). This evaluation practice reveals that TC products based on passive optical sensors tend to underestimate TC across all land cover types while Landsat-based TCs (i.e., NEX-TC & NLCD-TC) perform better than the coarser MODIS TC estimate. Our results show that the NEX-TC is generally comparable to NLCD-TC but it particularly outperforms NLCD-TC and MODIS-TC over the dense forests where lidar-TC indicates >80% TC. These results indicate that our hierarchical deep learning approach and TC product will be effective and useful for characterizing large-scale tree cover and possibly associated carbon dynamics.

Landsat↗

DOE OSTI2026

The Kinematically Hot, Extremely Metal-Poor C-19 Stellar Stream in DESI DR2

Stellar streams are the result of a host galaxy's gravitational potential tidally disrupting satellite dwarf galaxies and globular clusters (GCs), causing them to grow leading and trailing tidal tails. The C-19 stellar stream is an extremely metal-poor stellar population, showing chemical abundance patterns characteristic of a globular cluster. However, its large velocity dispersion is difficult to reconcile with a conventional, purely baryonic, disrupting-GC progenitor. Current techniques for stream characterization are primarily applied to Gaia DR3, relying heavily on proper motion measurements. Using the Dark Energy Spectroscopic Instrument (DESI), which provides radial velocities and metallicites for over 10 million stars reaching significantly fainter magnitudes than comparable surveys, we employ a mixture model approach to jointly characterize stream populations in proper motions, radial velocities, and metallicities against a Milky Way halo background. By applying this framework to the C-19 stellar stream, we identify a total of 47 spectroscopically confirmed member stars, of which 41 are newly identified and only 6 were previously reported in the literature. In this work, we measure a velocity dispersion of $7.8^{+1.5}_{-1.3}$ km s$^{-1}$ and a mean metallicity of [Fe/H] = $-3.36^{+0.12}_{-0.10}$. We further identify a novel 'spur' feature within the stream. We conclude that our measurements are in line with previous works identifying C-19 as a 'hot', metal-poor stream. In forthcoming work, we will apply this approach to many more streams in the DESI footprint, enabling population-level comparisons with predictions from simulations.

Mohammed, Nasser [Toronto U.] (ORCID:0009000812240↗

DOE OSTI2026

The Kinematically Hot, Extremely Metal-Poor C-19 Stellar Stream in DESI DR2

Mohammed, Nasser [Toronto U.] (ORCID:0009000812240↗

NASA NTRS2015

Using Dynamically Downscaled Climate Model Outputs to Inform Projections of Extreme Precipitation Events

Many of the storms that generate damaging floods are caused by locally intense, sub-daily precipitation, yet the spatial and temporal resolution of the most widely available climate model outputs are both too coarse to simulate these events. Thus there is often a disconnect between the nature of the events that cause damaging floods and the models used to project how climate change might influence their magnitude. This could be a particular problem when developing scenarios to inform future storm water management options under future climate scenarios. In this study we sought to close this gap, using sub-daily outputs from the Weather Research and Forecasting model (WRF) from each of the nine climate regions in the United States. Specifically, we asked 1) whether WRF outputs projected consistent patterns of change for sub-daily and daily precipitation extremes; and 2) whether this dynamically downscaled model projected different magnitudes of change for 3-hourly vs 24-hourly extreme events. We extracted annual maximum values for 3-hour through 24-hour precipitation totals from an 11-year time series of hindcast (1995-2005) and mid-century (2045-2055) climate, and calculated the direction and magnitude of change for 3-hour and 24-hour extreme events over this timeframe. The model results project that the magnitude of both 3-hour and 24-hour events will increase over most regions of the United States, but there was no clear or consistent difference in the relative magnitudes of change for sub-daily vs daily events.

Climate models↗

NASA NTRSDate not supplied

Advanced Weather Hazard Perception Capabilities for Remotely Piloted Aircraft in Wildland Fire Operations

Weather, specifically winds, are the most variable and least predictable of the weather-related elements that affect wildfire behavior.1 This leads to aviation operations which must deal with increased risks due to dynamic weather factors. Remotely piloted aircraft provide the opportunity to remove human loss of life from these risk consequences and mitigate the overall risks for air operations for fire suppression and dropping of retardant. UAS also allows the opportunity to continue fire suppression with nighttime operations. However, with the use of new technologies in aviation operations comes additional risk factors and the need for remote sensing capabilities to identify and permit navigation around extreme weather conditions, with turbulence being the most unforeseeable. This paper presents advanced concepts for weather hazard perception capabilities for remote pilot operators to include data and display techniques intended for dynamic environments and low altitude operations. This work can support the potential of weather sensing capabilities to create hazard maps to track high-risk weather patterns in mountainous regions. Advanced sensing may provide opportunity to locate extreme turbulence hazards for uninhabited aerial systems (UAS) and decrease the likelihood of flying into unknown weather conditions to enable safe operations to assist in wildfire air support.

Tyler L Willhite↗

NASA NTRS2007

Binary Black Holes, Numerical Relativity, and Gravitational Waves

The final merger of two black holes releases a tremendous amount of energy, more than the combined light from all the stars in the visible universe. This energy is emitted in the form of gravitational waves, and observing these sources with gravitational wave detectors such as LISA requires that we know the pattern or fingerprint of the radiation emitted. Since black hole mergers take place in regions of extreme gravitational fields, we need to solve Einstein's equations of general relativity on a computer in order to calculate these wave patterns. For more than 30 years, scientists have tried to compute these wave patterns. However, their computer codes have been plagued by problems that caused them to crash. This situation has changed dramatically in the past 2 years, with a series of amazing breakthroughs. This talk will take you on this quest for these gravitational wave patterns, showing how a spacetime is constructed on a computer to build a simulation laboratory for binary black hole mergers. We will focus on the recent advances that are revealing these waveforms, and the dramatic new potential for discoveries that arises when these sources will be observed by LISA

Centrella, Joan↗

NASA NTRS2007

Cosmic Messengers: Binary Black Holes and Gravitational Waves

The final merger of two black holes releases a tremendous amount of energy, more than the combined light from all the stars in the visible universe. This energy is emitted in the form of gravitational waves, and observing these sources with gravitational wave detectors such as LISA requires that we know the pattern or fingerprint of the radiation emitted. Since black hole mergers take place in regions of extreme gravitational fields, we need to solve Einstein s equations of general relativity on a computer in order to calculate these wave patterns. For more than 30 years, scientists have tried to compute these wave patterns. However, their computer codes have been plagued by problems that caused them to crash. . This situation has changed dramatically in the past 2 years, with a series of amazing breakthroughs. This talk will take you on this quest for these gravitational wave patterns, showing how a spacetime is constructed on a computer to build a simulation laboratory for binary black hole mergers. We will focus on the recent advances that are revealing these waveforms, and the dramatic new potential for discoveries that arises when these sources will. be observed by LISA.

Centrella, Joan↗

NASA NTRS2007

Binary Black Holes and Gravitational Waves

The final merger of two black holes releases a tremendous amount of energy, more than the combined light from all the stars in the visible universe. This energy is emitted in the form of gravitational waves, and observing these sources with gravitational wave detectors such as LIGO and LISA requires that we know the pattern or fingerprint of the radiation emitted. Since black hole mergers take place in regions of extreme gravitational fields, we need to solve Einstein's equations of general relativity on a computer in order to calculate these wave patterns. For more than 30 years, scientists have tried to compute these wave patterns. However, their computer codes have been plagued by problems that caused them to crash. This situation has changed dramatically in the past 2 years, with a series of amazing breakthroughs. This discussion examines these gravitational patterns, showing how a spacetime is constructed on a computer to build a simulation laboratory for binary black hole mergers. The focus is on recent advances that are revealing these waveforms, and the dramatic new potential for discoveries that arises when these sources will be observed by the space-based gravitational wave detector LISA.

Centrella, Joan↗

NASA NTRS2007

Binary Black Holes and Gravitational Waves

Centrella, Joan↗

NASA NTRS2010

Computing Cosmic Cataclysms

The final merger of two black holes releases a tremendous amount of energy, more than the combined light from all the stars in the visible universe. This energy is emitted in the form of gravitational waves, and observing these sources with gravitational wave detectors requires that we know the pattern or fingerprint of the radiation emitted. Since black hole mergers take place in regions of extreme gravitational fields, we need to solve Einstein's equations of general relativity on a computer in order to calculate these wave patterns. For more than 30 years, scientists have tried to compute these wave patterns. However, their computer codes have been plagued by problems that caused them to crash. This situation has changed dramatically in the past few years, with a series of amazing breakthroughs. This talk will take you on this quest for these gravitational wave patterns, showing how a spacetime is constructed on a computer to build a simulation laboratory for binary black hole mergers. We will focus on the recent advances that are revealing these waveforms, and the dramatic new potential for discoveries that arises when these sources will be observed.

Centrella, Joan M.↗

NASA NTRS1986

Ionospheric convection signatures observed by DE 2 during northward interplanetary magnetic field

Observations of the ionospheric convection signature at high latitudes are examined during periods of prolonged northward interplanetary magnetic field (IMF). The data from Dynamics Explorer 2 show that a four-cell convection pattern can frequently be observed in a region that is displaced to the sunward side of the dawn-dusk meridian regardless of season. In the eclipsed ionosphere, extremely structured or turbulent flow exists with no identifiable connection to a more coherent pattern that may simultaneously exist in the dayside region. The two highest-latitude convection cells that form part of the coherent dayside pattern show a dependence on the y component of the IMF. This dependence is such that a clockwise circulating cell displaced toward dawn dominates the high-latitude region when B(Y) is positive. Anti-clockwise circulation displaced toward dusk dominates the highest latitudes when B(Y) is negative. Examination of the simultaneously observed energetic particle environment suggests that both open and closed field lines may be associated with the high-latitude convection cells. On occasions these entire cells can exist on open field lines. The existence of closed field lines in regions of sunward flow is also apparent in the data.

Heelis, R. A.↗

DOE OSTI2025

Effect of Networking Density on the Patterning Performance of Molecular Layer Deposited Alucone Electron Beam/EUV Resists

One of the main barriers to continued device scaling in the era of extreme ultraviolet (EUV) lithography is the need for improved photoresist chemistries to address challenges such as poor EUV sensitivity, inadequate etch resistance, and pattern collapse. Metal-organic photoresists are a promising class of materials that can address many of these challenges, and among them, resists deposited via hybrid molecular layer deposition (MLD) have attracted interest for their unique advantages in thickness control, chemical homogeneity, and compatibility with vacuum processing. However, despite many successful demonstrations of patterning, little is known about how the molecular design of hybrid MLD resists affects their lithographic performance. In this work, we study the effect of the network structure, a common feature among all hybrid MLD resists, via a series of aluminum alkoxide ("alucone") negative tone resists with varying networking density. Their patterning mechanism is investigated via electron beam lithography (EBL)─a common proxy for EUV─and compared to their EUV-induced reactions studied via flood exposure and in situ characterization. In conclusion, we show that the resist with the least networking density demonstrates the best sensitivity and resolution, with the ability to resolve dense line/space gratings as small as 14 nm half pitch via EBL.

electron beam lithography↗

NASA NTRS2019

Rough-Wall Turbulent Heat Transfer Experiments in Hypersonic Free Flight

Experiments are being conducted in the NASA Ames Hypervelocity Free Flight Aerodynamic Facility to quantify the effects on turbulent convective heat transfer of surface roughness representative of a new class of 3D woven thermal protection system mRough-wall turbulent heat transfer measurements were obtained on ballistic-range models in hypersonic flight in the NASA Ames Hypervelocity Free Flight Aerodynamic Facility. Each model had three different surface textures on segments of the conic frustum: smooth wall, sand roughness, and a pattern roughness, thus providing smooth-wall and sand-roughness reference data for each test. The pattern roughness was representative of a woven thermal protection system material developed by NASA's Heatshield for Extreme Entry Environment Technology project. The tests were conducted at launch speeds of 3.2 km/s in air at 0.15 atm. Roughness Reynolds numbers, k+, ranged for 12 to 70 for the sand roughness, and as high as 200 for the pattern roughness. Boundary-layer parameters required for calculating k+ were evaluated using computational fluid dynamics simulations. The effects of pattern roughness are generally characterized by an equivalent sand roughness determined with a correlation developed from experimental data obtained on specifically-designed roughness patterns that do not necessarily resemble real TPS materials. Two sand roughness correlations were examined: Dirling and van Rij, et al. Both gave good agreement with the measured heat-flux augmentation for the two larger pattern roughness heights tested, but not for the smallest height tested. It has yet to be determined whether this difference is due to limitations in the experimental approach, or due to limits in the correlations used. Future experiments are planned that will include roughness patterns more like those used in developing the equivalent sand roughness correlations.aterials being developed by NASA's Heatshield for Extreme Entry Environment Technology (HEEET) project. Data were simultaneously obtained on sand-grain roughened surfaces and smooth surfaces, which can be compared with previously obtained data. Results are presented in this extended abstract for one roughness pattern. The full paper will include results from three roughness patterns representing virgin HEEET, nominal turbulent ablated HEEET, and twice the roughness of nominal turbulent ablated HEEET. Results will be used to compare with commonly used equivalent sand grain roughness correlations.

pattern roughness↗

NASA NTRS2013

Northern Eurasian Heat Waves and Droughts

This article reviews our understanding of the characteristics and causes of northern Eurasian summertime heat waves and droughts. Additional insights into the nature of temperature and precipitation variability in Eurasia on monthly to decadal time scales and into the causes and predictability of the most extreme events are gained from the latest generation of reanalyses and from supplemental simulations with the NASA GEOS-5 AGCM. Key new results are: 1) the identification of the important role of summertime stationary Rossby waves in the development of the leading patterns of monthly Eurasian surface temperature and precipitation variability (including the development of extreme events such as the 2010 Russian heat wave), 2) an assessment of the mean temperature and precipitation changes that have occurred over northern Eurasia in the last three decades and their connections to decadal variability and global trends in SST, and 3) the quantification (via a case study) of the predictability of the most extreme simulated heat wave/drought events, with some focus on the role of soil moisture in the development and maintenance of such events. A literature survey indicates a general consensus that the future holds an enhanced probability of heat waves across northern Eurasia, while there is less agreement regarding future drought, reflecting a greater uncertainty in soil moisture and precipitation projections. Substantial uncertainties remain in our understanding of heat waves and drought, including the nature of the interactions between the short-term atmospheric variability associated with such extremes and the longer-term variability and trends associated with soil moisture feedbacks, SST anomalies, and an overall warming world.

SST↗

DOE OSTI2025

CareWELL: Multimodal Region Representation Learning with Spatial Contexts for Urban Health

Rapid urbanization affects living environments by intensifying exposure to air pollution, heat, noise, and urban dynamics, which together contribute to uneven health outcomes across neighborhoods. For instance, cardiovascular, respiratory, and mental health conditions are each influenced by distinct exposures such as air pollution, extreme temperatures, or limited access to green space. These heterogeneous patterns require understanding the characteristics of geographic regions in order to explain why urban health risks vary across urban areas. Recent work in self-supervised region representation learning provides a promising way to model such characteristics from multimodal geospatial data. However, existing methods face two major limitations: (i) they often depend on non-public datasets, limiting reproducibility and applicability, and (ii) their generic pretraining objectives overlook health-relevant determinants, including temporal variability in environmental exposures and inequalities in social conditions. To address these gaps, we propose Context-Aware Region rEpresentation with Weather, Environment, and Location Learning (CareWELL). CareWELL leverages large language models to encode seasonal variability in weather, employs contrastive learning to align geo-coordinate and weather representations, and introduces a context-aware objective that integrates socio-demographic factors while preserving spatial correlations. We evaluate CareWELL by predicting six urban health outcomes in Manhattan, New York City, and demonstrate that CareWELL consistently outperforms state-of-the-art baselines as well as a traditional spatial computing method. These results suggest the importance of context-aware pretraining objectives for learning health-relevant region representations.

Namgung, Min [ORNL]↗