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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

The formation and annealing of circumstellar dust, as gauged by IRAS low-resolution spectra and the microwave maser chronology

By comparing IRAS mean colors and LRS spectral differences in a large sample of Mira variables with the chronological sequence for development of microwave maser emission among such stars, it appears possible to describe a dust grain formation and annealing sequence. The early time spectra are dominated by aluminum oxides, which are then overrun by the emergence of silicates. Rather than the growth of silicate mantles on aluminate cores, this sequence is interpreted as a simple result of the higher electron affinity of aluminum for oxygen, resulting in an initial abundance of AlO bonds in the underoxidized grains. Once all the Al becomes fully oxidized, the relative abundances of Al and Si dictate that the AlO signature will be swamped by the growth of the SiO stretching fundamental. The correlations among the proposed dust chronology, the increasingly thick circumstellar envelopes, and the light curve asymmetries of Mira variables are instructive for understanding the evolutionary changes occurring in such stars.

Stencel, Robert E.↗
NASA NTRS1991

IRAS low-resolution spectral observations of the 10 and 18 micron silicate emission feature

The IRAS LRS Atlas contains 8-23 micron spectra of 1816 stars with silicate emission features. In order to study the shapes of the silicate dust features, the 117 stars whose emission features appear to be optically thin and which have the best signal-to-noise ratio at the longest wavelengths were analyzed. Simple spherical dust shell models were calculated in both the optically thin and the slightly optical thick approximations. From the comparison of the predicted spectra of the dust and stellar continuum, the emissivity function Kappa(lambda) was derived. In the different stars, the shape of the 10-micron feature is either narrow or broad and it is peaked either at about 9.7 microns or at 10 microns. Either particle size effects (particles above 0.75 microns in radius) or optical depth effects (central optical depth about 1) could broaden the 10-micron feature. Chemical composition differences no doubt are also important, particularly as regards the position of the peak of the 10-micron feature. The stars with the peak at 10 microns are more closely confined to the Galactic plane than the stars with the peak at 9.7 microns. The shape of the 18-micron feature is essentially the same in all stars, and can be used to extend the interstellar extinction curve past 13 to 22 microns.

Simpson, Janet P.↗
NASA NTRS1998

A Modeling Approach to Global Land Surface Monitoring with Low Resolution Satellite Imaging

The effects of changing land use/land cover on global climate and ecosystems due to greenhouse gas emissions and changing energy and nutrient exchange rates are being addressed by federal programs such as NASA's Mission to Planet Earth (MTPE) and by international efforts such as the International Geosphere-Biosphere Program (IGBP). The quantification of these effects depends on accurate estimates of the global extent of critical land cover types such as fire scars in tropical savannas and ponds in Arctic tundra. To address the requirement for accurate areal estimates, methods for producing regional to global maps with satellite imagery are being developed. The only practical way to produce maps over large regions of the globe is with data of coarse spatial resolution, such as Advanced Very High Resolution Radiometer (AVHRR) weather satellite imagery at 1.1 km resolution or European Remote-Sensing Satellite (ERS) radar imagery at 100 m resolution. The accuracy of pixel counts as areal estimates is in doubt, especially for highly fragmented cover types such as fire scars and ponds. Efforts to improve areal estimates from coarse resolution maps have involved regression of apparent area from coarse data versus that from fine resolution in sample areas, but it has proven difficult to acquire sufficient fine scale data to develop the regression. A method for computing accurate estimates from coarse resolution maps using little or no fine data is therefore needed.

Hlavka, Christine A.↗
DOE OSTI2025

Super-Resolution for Renewable Energy Resource Data with Wind from Reanalysis Data and Application to Ukraine

With a potentially increasing share of the electricity grid relying on wind to provide generating capacity and energy, there is an expanding global need for historically accurate, spatiotemporally continuous, high-resolution wind data. Conventional downscaling methods for generating these data based on numerical weather prediction have a high computational burden and require extensive tuning for historical accuracy. In this work, we present a novel deep learning-based spatiotemporal downscaling method using generative adversarial networks (GANs) for generating historically accurate high-resolution wind resource data from the European Centre for Medium-Range Weather Forecasting Reanalysis version 5 data (ERA5). In contrast to previous approaches, which used coarsened high-resolution data as low-resolution training data, we use true low-resolution simulation outputs. We show that by training a GAN model with ERA5 as the low-resolution input and Wind Integration National Dataset Toolkit (WTK) data as the high-resolution target, we achieved results comparable in historical accuracy and spatiotemporal variability to conventional dynamical downscaling. This GAN-based downscaling method additionally reduces computational costs over dynamical downscaling by two orders of magnitude. We applied this approach to downscale 30 km, hourly ERA5 data to 2 km, 5 min wind data for January 2000 through December 2023 at multiple hub heights over Ukraine, Moldova, and part of Romania. With WTK coverage limited to North America from 2007–2013, this is a significant spatiotemporal generalization. The geographic extent centered on Ukraine was motivated by stakeholders and energy-planning needs to rebuild the Ukrainian power grid in a decentralized manner. This 24-year data record is the first member of the super-resolution for renewable energy resource data with wind from the reanalysis data dataset (Sup3rWind).

17 WIND ENERGY↗
NASA NTRS1985

Retinally stabilized differential resolution television display

A remote television viewing system employing an eye tracker is disclosed, wherein a small region of the image appears in high resolution, and the remainder of the image appears in low resolution. The eye tracker monitors the position of the viewer's line of sight. The eye tracker position data is transmitted to the remote television camera and control. Both the remote camera and television display are adapted to have selectable high-resolution and low resolution raster scan modes. The position data from the eye tracker is used to determine the point at which the high-resolution scan is to commence. The video data defining the observed image is encoded in a novel format, wherein in each data field, the data representing the position of the high resolution region of predetermined size appears first, followed by the high resolution zone video data and then the low-resolution region data. As the viewer's line of sight relative to the displayed image changes, the position of the high resolution region changes to track the viewer's line of sight.

Ruoff, C. F., Jr.↗
NASA NTRS2004

Radio Frequency Interference (RFI) in Digital Microwave Radiometers

Here we developed a model for determining the effects of narrowband RFI on low resolution digital correlators. Low resolution correlators rely on a theoretical inversion to obtain the input correlation coefficient from the digital output. This inversion is based on the Gaussian statistics of the input signals. In the presence of narrow-band interference, the statistics are not Gaussian and the theoretical inversion is no longer valid. The result is an error in the correlator output. We studied this phenomena for four correlator resolutions: 1, 1.5, 2, and 4 bits. The errors are significant for 1, 1.5, and 2-bit systems. We found in the presence of relatively strong interference (INR approximately greater than 0 dB) the errors can be ten's of percent. The error reduces to less than 0.03% for INR less than -16 dB. For the four-bit correlators, the errors are less than 0.03% for all cases studied. The error is also nonlinearly dependent upon input correlation coefficient.

Piepmeier, Jeffrey R.↗
DOE OSTI2021

Enriching Load Data Using Micro-PMUs and Smart Meters

In modern distribution systems, load uncertainty can be fully captured by micro-PMUs, which can record high-resolution data; however, in practice, micro-PMUs are installed at limited locations in distribution networks due to budgetary constraints. In contrast, smart meters are widely deployed but can only measure relatively low-resolution energy consumption, which cannot sufficiently reflect the actual instantaneous load volatility within each sampling interval. In this paper, we have proposed a novel approach for enriching load data for service transformers that only have low-resolution smart meters. The key to our approach is to statistically recover the high-resolution load data, which is masked by the low-resolution data, using trained probabilistic models of service transformers that have both high- and low-resolution data sources, i.e., micro-PMUs and smart meters. The overall framework consists of two steps: first, for the transformers with micro-PMUs, a Gaussian Process is leveraged to capture the relationship between the maximum/minimum load and average load within each low-resolution sampling interval of smart meters; a Markov chain model is employed to characterize the transition probability of known high-resolution load. Next, the trained models are used as teachers for the transformers with only smart meters to decompose known low-resolution load data into targeted high-resolution load data. The enriched data can recover instantaneous load uncertainty and significantly enhance distribution system observability and situational awareness. Here, we have verified the proposed approach using real high- and low-resolution load data.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Impact of Tropical Cyclone Wind Forcing on the Global Climate in a Fully Coupled Climate Model

Tropical cyclones (TCs) alter upper-ocean temperature and influence ocean heat content via enhanced turbulent mixing. A better understanding of the role of TCs within the climate system requires a fully coupled modeling framework, where TC-induced ocean responses feed back to the atmosphere and subsequently to the climate mean state and variability. Here, we investigate the impacts of TC wind forcing on the global ocean and the associated feedbacks within the climate system using the fully coupled Community Earth System Model version 1.3 (CESM1.3). Using the low-resolution version of CESM1.3 (1° atmosphere and ocean grid spacing) with no intrinsic TCs, we conduct a suite of sensitivity experiments by inserting TC winds extracted from a high-resolution (0.25° atmosphere grid spacing) TC-permitting simulation into the low-resolution model. Results from the low-resolution TC experiment are compared to a low-resolution control simulation to diagnose TCs’ impact. We found that the added TC winds can increase ocean heat content by affecting ocean vertical mixing, air–sea enthalpy fluxes, and cloud amount. Here, the added TCs can influence mean SST, precipitation, ocean subsurface temperature, and ocean mixed layer depth. We found a strengthening of the wind-driven subtropical cells and a weakening of the Atlantic meridional overturning circulation due to the changes of surface buoyancy fluxes. TCs in the model cause anomalous equatorward ocean heat convergence in the deep tropics and an increase of poleward ocean heat transport out of the subtropics. Our modeling results provide new insights into the multiscale interactions between TCs and the coupled climate system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Reducing Long‐Standing Surface Ozone Overestimation in Earth System Modeling by High‐Resolution Simulation and Dry Deposition Improvement

The overestimation of surface ozone concentration in low‐resolution global atmospheric chemistry and climate models has been a long‐standing issue. We first update the ozone dry deposition scheme in both high‐ (0.25°) and low‐resolution (1°) Community Earth System Model (CESM) version 1.3 runs, by adding the effects of leaf area index and correcting the sunlit and shaded fractions of stomatal resistances. With this update, 5‐year‐long summer simulations (2015–2019) using the low‐resolution CESM still exhibit substantial ozone overestimation (by 6.0–16.2 ppbv) over the U.S., Europe, eastern China, and ozone pollution hotspots. The ozone dry deposition scheme is further improved by adjusting the leaf cuticle conductance, reducing the mean ozone bias by 19%, and increasing the model resolution further reduces the ozone overestimation by 43%. We elucidate the mechanism by which model grid spacing influences simulated ozone, revealing distinctive pathways in urban versus rural areas. In rural areas, grid spacing mainly affects daytime ozone levels, where additional NO x emissions from nearby urban areas result in an ozone boost and overestimation in low‐resolution simulations. In contrast, over urban areas, daytime ozone overestimation follows a similar mechanism due to the influence of volatile organic compounds from surrounding rural areas. However, nighttime ozone overestimation is closely linked to weakened NO titration owing to the redistribution of urban NO x to rural areas. Additionally, stratosphere‐troposphere exchange may also contribute to reducing ozone bias in high‐resolution simulations, warranting further investigation. This optimized high‐resolution CESM may enhance understanding of ozone formation mechanisms, sources, and changes in a warming climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Using 2.5D super-resolution to improve flaw detection in metal additive manufacturing parts

Industrial X-ray computed tomography (XCT) enables non-destructive inspection of additively manufactured (AM) parts, but high-resolution scanning requires long acquisition times and significant computational resources, limiting throughput in production environments. Super-resolution techniques can recover high-resolution information from low-resolution scans, but existing methods face a trade-off between 2D approaches that ignore inter-slice information and 3D methods that are computationally prohibitive for practical deployment. To address this trade-off, we propose a 2.5D deep learning-based super-resolution approach that uses seven neighbouring low-resolution slices to super-resolve the centre slice. This work evaluates the method on real XCT scans of steel AM parts, comparing reconstruction quality and flaw detection performance of 2D, 2.5D, and 3D ESRGAN-based super-resolution methods. Results demonstrate that 2.5D super-resolution significantly improves detection of small, process-induced flaws (e.g. porosity) compared to 2D methods, while avoiding the prohibitive computational burden of full 3D approaches. These findings provide initial evidence of 2.5D super-resolution as a practical, deployable solution for improving flaw detection in high-throughput industrial XCT inspection.

X-ray CT↗
NASA NTRS1973

Radar/radiometer facilities for precipitation measurements

The OSU ElectroScience Laboratory Radar/Radiometer Facilities are described. This instrumentation includes a high-resolution radar/radiometer system, a fully automated low-resolution radar system, and a small surveillance radar system. The high-resolution radar/radiometer system operates at 3, 9, and 15 GHz using two 9.1 m and one 4.6 m parabolic antennas, respectively. The low-resolution and surveillance radars operate at 9 and 15 GHz, respectively. Both the high- and low-resolution systems are interfaced to real-time digital processing and recording systems. This capability was developed for the measurement of the temporal and spatial characteristics of precipitation in conjunction with millimeter wavelength propagation studies utilizing the Advanced Technology Satellites. Precipitation characteristics derived from these measurements could also be of direct benefit in such diverse areas as: the atmospheric sciences, meteorology, water resources, flood control and warning, severe storm warning, agricultural crop studies, and urban and regional planning.

Hodge, D. B.↗
NASA NTRS2018

NeMO-Net - The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. By combining spatial and spectral information from varying resolutions, we seek to augment and improve the classification accuracy of previously low-resolution datasets at large temporal scales.NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive learning and training software, currently being developed at NASA Ames, is aimed at assessing the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. The latest iteration uses fully convolutional networks to segment and identify coral imagery taken by UAVs and satellites, including WorldView-2 and Sentinel. We present results taken from the Indian Ocean where classification accuracy has exceeded 91% for 24 geomorphological classes given ample training data. In addition, we utilize deep Laplacian Pyramid Super-Resolution Networks (LapSRN) to reconstruct high resolution information from low resolution imagery, trained from various UAV and satellite datasets. Finally, in the case of insufficient training data, we have developed an interactive online platform that allows users to easily segment and submit their classifications, which has been integrated with the current NeMO-Net workflow. Specifically, we present results from the Fiji islands in which preliminary user data has allowed for the accurate identification of 9 separate classes, despite issues such as cloud shadowing and spectral variation. The project is being supported by NASA's Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

Neural↗
NASA NTRS2014

Discovery of Finely Structured Dynamic Solar Corona Observed in the Hi-C Telescope

In the summer of 2012, the High-resolution Coronal Imager (Hi-C) flew aboard a NASA sounding rocket and collected the highest spatial resolution images ever obtained of the solar corona. One of the goals of the Hi-C flight was to characterize the substructure of the solar corona. We therefore examine how the intensity scales from AIA resolution to Hi-C resolution. For each low-resolution pixel, we calculate the standard deviation in the contributing high-resolution pixel intensities and compare that to the expected standard deviation calculated from the noise. If these numbers are approximately equal, the corona can be assumed to be smoothly varying, i.e. have no evidence of substructure in the Hi-C image to within Hi-C's ability to measure it given its throughput and readout noise. A standard deviation much larger than the noise value indicates the presence of substructure. We calculate these values for each low-resolution pixel for each frame of the Hi-C data. On average, 70 percent of the pixels in each Hi-C image show no evidence of substructure. The locations where substructure is prevalent is in the moss regions and in regions of sheared magnetic field. We also find that the level of substructure varies significantly over the roughly 160 s of the Hi-C data analyzed here. This result indicates that the finely structured corona is concentrated in regions of heating and is highly time dependent.

Winebarger, A.↗
NASA NTRS2009

A New Spectroscopic and Interferometric Study of the Young Stellar Object V645 Cyg

Aims. We present the results of high-resolution optical spectroscopy, low-resolution near-IR spectroscopy and near-infrared speckle interferometry of the massive young stellar object candidate V645 Cyg that were taken in order to refine its fundamental parameters and properties of its circumstellar envelope. Methods. Speckle interferometry in the H- and K-bands and an optical spectrum in the range 5200-6680 A with a spectral resolving power of R = 60000 were obtained at the 6 m telescope of the Russian Academy of Sciences. Another optical spectrum in the range 4300-10500 A with R = 79000 was obtained at the 3.6m CFHT. A low-resolution spectrum in the ranges 0.46-1.4 and 1.4-2.5 microns with a R approx. 800 and approx. 700, respectively, were obtained at the 3m Shain telescope of the Lick Observatory. Results. Using a new kinematic method based on non-linear modeling of the neutral hydrogen density profile in the direction toward the object, we suggest a new a distance D = 4.2+/-0.2 kpc. We also suggest a new estimate for the star's effective temperature, T(sub eff) approx. 25000 K. We have resolved the object in both H- and K-bands. Using a two-component ring fit, we derived a compact component size of 18 mas and 15 mas in the H- and K-band, respectively, which correspond to 37 and 33 AU at the new distance. Analysis of our and previously published data shows a approx. 2 mag drop of the near-infrared brightness of V645 Cyg in the beginning of the 1980 s. At the same time, the cometary nebular condensation N1 seems to fade in this wavelength range with respect to the N0 object, which represent the star with a nearly pole-on optically-thick disk and an optically-thin envelope. Conclusions. We conclude that V645 Cyg is a young massive main-sequence star, which recently emerged from its cocoon. and already passed the protostellar accretion stage. The presence of accretion is not necessary to account for the high observed luminosity of (2.6) x 10(exp 4) Solar Mass/yr. The receding part of a strong, mostly uniform outflow with a terminal velocity of approx.800 km/s is only blocked from view far from the star, where forbidden lines form. The near-infrared size of the source is consistent with the dust sublimation distance near this hot and luminous star and is the largest among young stellar objects observed interferometrically to-date.

Miroshinichenko, A. S.↗