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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Visual sensitivity to spatially sampled modulation in human observers

Thresholds were measured for detecting spatial luminance modulation in regular lattices of visually discrete dots. Thresholds for modulation of a lattice are generally higher than the corresponding threshold for modulation of a continuous field, and the size of the threshold elevation, which depends on the spacing of the lattice elements, can be as large as a one log unit. The largest threshold elevations are seen when the sample spacing is 12 min arc or greater. Theories based on response compression cannot explain the further observation that the threshold elevations due to spatial sampling are also dependent on modulation frequency: the greatest elevations occur with higher modulation frequencies. The idea that this is due to masking of the modulation frequency by the spatial frequencies in the sampling lattice is considered.

Mulligan, Jeffrey B.↗
NASA NTRS1976

Additional studies of forest classification accuracy as influenced by multispectral scanner spatial resolution

First, an analysis of forest feature signatures was used to help explain the large variation in classification accuracy that can occur among individual forest features for any one case of spatial resolution and the inconsistent changes in classification accuracy that were demonstrated among features as spatial resolution was degraded. Second, the classification rejection threshold was varied in an effort to reduce the large proportion of unclassified resolution elements that previously appeared in the processing of coarse resolution data when a constant rejection threshold was used for all cases of spatial resolution. For the signature analysis, two-channel ellipse plots showing the feature signature distributions for several cases of spatial resolution indicated that the capability of signatures to correctly identify their respective features is dependent on the amount of statistical overlap among signatures. Reductions in signature variance that occur in data of degraded spatial resolution may not necessarily decrease the amount of statistical overlap among signatures having large variance and small mean separations. Features classified by such signatures may thus continue to have similar amounts of misclassified elements in coarser resolution data, and thus, not necessarily improve in classification accuracy.

Sadowski, F. E.↗
NASA NTRS1980

The spatial-temporal ambiguity in auroral modeling

The paper examines the time-dependent models of the aurora which show that various ionospheric parameters respond to the onset of auroral ionization with different time histories. A pass of the Atmosphere Explorer C satellite over Poker Flat, Alaska, and ground based photometric and photographic observations have been used to resolve the time-space ambiguity of a specific auroral event. The density of the O(+), NO(+), O2(+), and N2(+) ions, the electron density, and the electron temperature observed at 280 km altitude in a 50 km wide segment of an auroral arc are predicted by the model if particle precipitation into the region commenced about 11 min prior to the overpass.

Rees, M. H.↗
NASA NTRSDate not supplied

Optimizing spectroheliogram deconvolution methods : MaGIXS - A case study

Over the past five years, new methods to reconstruct spectrally pure maps of the Sun from spectroheliogram data have emerged. In particular, with the rebirth of long-abandoned slitless imaging spectroscopy, one may obtain both spatial and spectral information over a large field of view simultaneously. Yet, depending on the size of the extended source combined with the extent of spectral dispersion, there will be locations in the focal plane where spectral lines from different spatial locations overlap and must be deconvolved. An unfolding method has been successfully developed and demonstrated on the recent rocket flight MaGIXS, which observed several strong emission lines (9 to 30$\AA$) from different portions of two active regions. In the work we are going to present, we conduct a systematic investigation of the parameters that controls and optimizes the inversion method to unfold overlappogram data. We also demonstrate a derived method of the inversion that does not require previous assumptions on the thermal and ionization equilibrium and abundance state of the plasma.

Spectroheliogram↗
NASA NTRS2006

Estimating Temperature Retrieval Accuracy Associated With Thermal Band Spatial Resolution Requirements for Center Pivot Irrigation Monitoring and Management

This study explores the use of synthetic thermal center pivot irrigation scenes to estimate temperature retrieval accuracy for thermal remote sensed data, such as data acquired from current and proposed Landsat-like thermal systems. Center pivot irrigation is a common practice in the western United States and in other parts of the world where water resources are scarce. Wide-area ET (evapotranspiration) estimates and reliable water management decisions depend on accurate temperature information retrieval from remotely sensed data. Spatial resolution, sensor noise, and the temperature step between a field and its surrounding area impose limits on the ability to retrieve temperature information. Spatial resolution is an interrelationship between GSD (ground sample distance) and a measure of image sharpness, such as edge response or edge slope. Edge response and edge slope are intuitive, and direct measures of spatial resolution are easier to visualize and estimate than the more common Modulation Transfer Function or Point Spread Function. For these reasons, recent data specifications, such as those for the LDCM (Landsat Data Continuity Mission), have used GSD and edge response to specify spatial resolution. For this study, we have defined a 400-800 m diameter center pivot irrigation area with a large 25 K temperature step associated with a 300 K well-watered field surrounded by an infinite 325 K dry area. In this context, we defined the benchmark problem as an easily modeled, highly common stressing case. By parametrically varying GSD (30-240 m) and edge slope, we determined the number of pixels and field area fraction that meet a given temperature accuracy estimate for 400-m, 600-m, and 800-m diameter field sizes. Results of this project will help assess the utility of proposed specifications for the LDCM and other future thermal remote sensing missions and for water resource management.

Ryan, Robert E.↗
NASA NTRSDate not supplied

A Novel Machine Learning Method for Surface PM2.5 Estimations from Geostationary Satellites

Particulate matter (PM) with a diameter of less or equal to 2.5 μm, known as PM , affects human health as it penetrates the respiratory system. The Environmental Protection Agency (EPA) measures the atmospheric concentration of PM using air quality monitors stationed throughout the Continental United States (CONUS). Such measurements are points on a spatial domain and therefore, might not be representative of the air quality at nearby areas considering that the composition of the atmosphere is highly variable from place to place. Satellite based AOD permits a spatially uniform means of estimating PM and new geostationary satellites provide high temporal and spatial resolution estimation of AOD. However, the concentration of PM is non-linearly dependent on other atmospheric parameters that include relative humidity, temperature, and height of the planetary boundary layer. This information may be estimated at similar spatial and temporal resolutions as AOD from numerical modeling such as from the National Oceanic and Atmospheric Administration’s (NOAA) High Resolution Rapid Refresh (HRRR) model which resolves near real-time atmospheric conditions over the CONUS. The estimation of PM concentration is a multi-parametric problem that considers the effect of temporal dependencies among the different parameters. Deep learning approaches are appropriate for such complex estimation problems as they intrinsically capture relations among multiple non-linear parameters. This study compares deep-learning methods to traditional regression analysis to demonstrate the capabilities of these methods in predicting PM2.5 concentrations. Additionally, a novel ensemble learning approach is employed to identify scientific processes that could further improve the estimation of PM concentration. Utilizing Long Short-Term Memory (LSTM) neural networks, which are suitable for multivariate time series estimation problems as they are capable of learning long-term dependencies, individual models are created for each EPA station and trained on the aforementioned dataset collocated over each station. Individual station models are merged if the model's performance is improved by reducing the root mean squared error (RMSE) metric. This ensemble training method ultimately reduces the RMSE value. Evaluation of these results provide insights into physical processes and related observable parameters that may contribute to PM concentrations. Identified parameters evaluated to be statistically different between the merged and unmerged models are expected to improve overall performance. These new parameters are then utilized for reevaluation of the deep learning methods with an extreme gradient boosting model with an RMSE of 5.5 providing the best results.

George Priftis↗
DOE OSTI2023

Dissect two-halo galactic conformity effect for central galaxies: the dependence of star formation activities on the large-scale environment

We investigate the two-halo galactic conformity effect for central galaxies, which is the spatial correlation of the star formation activities for central galaxies to several Mpcs, by studying the dependence of the star formation activities of central galaxies on their large-scale structure in our local Universe using the SDSS data. Here we adopt a novel environment metric using only central galaxies quantified by the distance to the nth nearest central galaxy. This metric measures the environment within an aperture from ∼1 to ≳ 10 Mpc, with a median value of ∼4 Mpc. We found that two kinds of conformity effects in our local Universe. The first one is that low-mass central galaxies are more quenched in high-density regions, and we found that this effect mainly comes from low-mass centrals that are close to a more massive halo. A similar trend is also found in the IllustrisTNG simulation, which can be entirely explained by backsplash galaxies. The second conformity effect is that massive central galaxies in low-density regions are more star-forming. This population of galaxies also possesses a higher fraction of spiral morphology and lower central stellar velocity dispersion, suggesting that their low quiescent fraction is due to less-frequent major merger events experienced in the low-density regions and, as a consequence, less-massive bulges and central black holes.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1985

Utilizing remote sensing of Thematic Mapper data to improve our understanding of estuarine processes and their influence on the productivity of estuarine-dependent fisheries

The purpose of the project is to refine and validate a probabilistic spatial computer model through the analyses of thematic mapper imagery. The model is designed to determine how the interface between marshland and water changes as marshland is converted to water in a disintegrating marsh. Coastal marshland in Louisiana is disintegrating at the rate of approximately 40 sq mi a year, and an evaluation of the potential impact of this loss on the landings of estuarine-dependent fisheries is needed by fisheries managers. Understanding how marshland-water interface changes as coastal marshland is lost is essential to the process of evaluating fisheries effects, because several studies suggest that the production of estuarine-dependent fish and shellfish may be more closely related to the interface between marshland and water than to acreage of marshland. The need to address this practical problem has provided an opportunity to apply some scientifically interesting new techniques to the analyses of satellite imagery. Progress with the development of these techniques is the subject of this report.

Browder, Joan A.↗
NASA NTRS1993

Theory of Metastable State Relaxation for Non-Critical Binary Systems with Non-Conserved Order Parameter

A new mathematical ansatz for a solution of the time-dependent Ginzburg-Landau non-linear partial differential equation is developed for non-critical systems such as non-critical binary solutions (solute + solvent) described by the non-conserved scalar order parameter. It is demonstrated that in such systems metastability initiates heterogeneous solute redistribution which results in formation of the non-equilibrium singly-periodic spatial solute structure. It is found how the time-dependent period of this structure evolves in time. In addition, the critical radius r(sub c) for solute embryo of the new solute rich phase together with the metastable state lifetime t(sub c) are determined analytically and analyzed.

Izmailov, Alexander↗
NASA NTRS2015

Simulation and Analysis of Technology and Operational Procedures to Reduce the Combined Effects of Emissions and Contrails

The development and evaluation of concepts and technology to support future air traffic management systems require a hierarchy of models ranging from real-time simulations to extensive field evaluations. Air traffic simulation models such as Airspace Concept Evaluation System, Center Tracon Automation System, Future Air traffic management Concept Evaluation Tool and others are used to design air traffic systems balancing the conflicting objectives of maximizing safety, meeting future demands for airports and airspace and increase efficiency of traffic flows in the presence of uncertain weather. The impact of aviation emissions and contrails on climate imposes another constraint on the design of aircraft and aviation operations. The understanding of the complex interaction between physical climate system, the carbon and other greenhouse gas emissions and aviation activity can be improved by the development of integrated assessment models that include emission and climate models together with air traffic simulations. The modeling of aircraft emissions and their interaction with each other to change the concentration levels of different gasses in the atmosphere and the resulting impact of the radiative forcing on the equilibrium of the Earth's atmosphere is complex and requires the use of coupled atmosphere-ocean general circulation models together with three-dimensional models of carbon cycle and chemistry of other non-CO2 greenhouse gases. These models are computationally intensive and unsuitable for studies involving the generation of multiple scenarios. Simple emission and climate models, based on the input-output relations of linear systems, capture the fundamental emission to climate impact behavior by careful selection of key variables and their dynamics. The impact of various greenhouse gases depends on the total concentration, effect per unit change in atmospheric concentration and the spatial distribution of the gas. All these quantities are influenced by the lifetime of the gas. The impact of a greenhouse gas depends on the interval of assessment, which may vary from a few decades to a few centuries. Climate metrics are aimed at providing a common scale to compare different greenhouse gases. If the metrics are to be used as a tool in developing and evaluating aviation operations, they should be transparent and easy to apply. Global Warming Potential and Aggregate Global Temperature Potential are some of the commonly used metrics. This paper integrates a national-level air traffic simulation and optimization capability with simple climate models and carbon cycle models, and climate metrics to assess the impact of aviation on climate. The capability brings together metrics, which are useful in aviation operations together with metrics used in climate studies. The capability can be used to make trade-offs between extra fuel cost and reduction in climate impact. There is considerable uncertainty in our understanding of the radiative forcing associated with emissions and contrails. The parameters in the simulation can be used to evaluate the effect of various uncertainties in emission models and contrails. It can also be used to evaluate the impact of different decision horizons. Alternatively, the optimization results from the simulation can be used as inputs to other tools that monetize global climate impacts like the FAA's Aviation Environmental Portfolio Management Tool for Impacts.

impact of aviation on the environment↗
DOE OSTI2024

Resolving length-scale-dependent transient disorder through an ultrafast phase transition

Abstract Material functionality can be strongly determined by structure extending only over nanoscale distances. The pair distribution function presents an opportunity for structural studies beyond idealized crystal models and to investigate structure over varying length scales. Applying this method with ultrafast time resolution has the potential to similarly disrupt the study of structural dynamics and phase transitions. Here we demonstrate such a measurement of CuIr 2 S 4 optically pumped from its low-temperature Ir-dimerized phase. Dimers are optically suppressed without spatial correlation, generating a structure whose level of disorder strongly depends on the length scale. The redevelopment of structural ordering over tens of picoseconds is directly tracked over both space and time as a transient state is approached. This measurement demonstrates the crucial role of local structure and disorder in non-equilibrium processes as well as the feasibility of accessing this information with state-of-the-art XFEL facilities.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Extreme Scale Infrasound Inversion and Prediction for Weather Characterization and Acute Event Detection

Accurate and timely weather predictions are critical to many aspects of society with a profound impact on our economy, general well-being, and national security. In particular, our ability to forecast severe weather systems is necessary to avoid injuries and fatalities, but also important to minimize infrastructure damage and maximize mitigation strategies. The weather community has developed a range of sophisticated numerical models that are executed at various spatial and temporal scales in an attempt to issue global, regional, and local forecasts in pseudo real time. The accuracy however depends on the time period of the forecast, the nonlinearities of the dynamics, and the target spatial resolution. Significant uncertainties plague these predictions including errors in initial conditions, material properties, data, and model approximations. To address these shortcomings, a continuous data collection occurs at an effort level that is even larger than the modeling process. It has been demonstrated that the accuracy of the predictions depends on the quality of the data and is independent to a certain extent on the sophistication of the numerical models. Data assimilation has become one of the more critical steps in the overall weather prediction business and consequently substantial improvements in the quality of the data would have transformational benefits. This paper describes the use of infrasound inversion technology, enabled through exascale computing, that could potentially achieve orders of magnitude improvement in data quality and therefore transform weather predictions with significant impact on many aspects of our society.

58 GEOSCIENCES↗
NASA NTRS1989

A practical approach to the classification of IRAS sources using infrared colors alone

Zones of the IRAS color-color planes in which a variety of different types of known source occur, have been defined for the purpose of obtaining representative IRAS colors for them. There is considerable overlap between many of these zones, rendering a unique classification difficult on the basis of IRAS colors alone, although galactic latitude can resolve ambiguities between galactic and extragalactic populations. The color dependence of these zones on the presence of spectral emission/absorption features and on the spatial extent of the sources has been investigated. It is found that silicate emission features do not significantly influence the IRAS colors. Planetary nebulae may show a dependence of color on the presence of atomic or molecular features in emission, although the dominant cause of this effect may be the underlying red continua of nebulae with strong atomic lines. Only small shifts are detected in the colors of individual spatially extended sources when total flux measurements are substituted for point-source measurements.

Walker, Helen J.↗
NASA NTRS1986

The cluster-cluster correlation function

The clustering properties of the Abell and Zwicky cluster catalogs are studied using the two-point angular and spatial correlation functions. The catalogs are divided into eight subsamples to determine the dependence of the correlation function on distance, richness, and the method of cluster identification. It is found that the Corona Borealis supercluster contributes significant power to the spatial correlation function to the Abell cluster sample with distance class of four or less. The distance-limited catalog of 152 Abell clusters, which is not greatly affected by a single system, has a spatial correlation function consistent with the power law Xi(r) = 300r exp -1.8. In both the distance class four or less and distance-limited samples the signal in the spatial correlation function is a power law detectable out to 60/h Mpc. The amplitude of Xi(r) for clusters of richness class two is about three times that for richness class one clusters. The two-point spatial correlation function is sensitive to the use of estimated redshifts.

Postman, M.↗
NASA NTRS1994

Topography and subduction geometry in the central Andes: Clues to the mechanics of a noncollisional orogen

The central Andeean orogen between 12 deg and 32 deg S latitude exhibits a high degree of spatial order: principally an extraordinary bilateral symmetry that is common to the Earth's surface, the underlying Wadati-Benioff zone, and the Nazca/South America plate kinematics, which has been stable since the mid-Tertiary. This spatial order must reflect the physical mechanisms of mountain building in this noncollisional orogen. The shapes of the topography and subduction zone can be reduced to symmetric and antisummeric components relative to any verical symmetry plane; the particular plaen which minimizes the antisymmetry (and maximizes the symmetry) is well resolved and is essentially coincident with the stable Euler equator of Nacza/South America relative motion since the mid-Tertiary. That the topography, subduction geometry, and persistent mid-Tertiary plate kinematics share common spatial and geometric elements suggests that he distribution of topography in this orogen depends strongly on the dynamics of subduction. Other factors that might affect the topography and underlying tectonics, such as climate and inherited strutura fabric, which have different spatial characterisitcs, must be of less significance at a continental scale. Furthermore, the small components of asymmetry among the various elements of the orogen appear to be mutually relate in a simple way; it is possible that this coupled asymmetry is associated with a late Teriary change in plate kinematics. These observations suggest that there is a close connection between plate tectonics and the form of the Earth's surface in this noncollisional setting. It follows hta the distribution of topography near convergent plate boundaries may provide a powerful constraing for understanding the dynamics of subduction.

Gephart, John W.↗
NASA NTRS1999

Cloud Power Spectra-Dependence on Solar Zenith Angle and Wavelength, Implications for Cloud Optical Property Retrievals

Scale breaks (spatial scales at which power-law exponent changes occur) observed in Landsat radiances have proven to be useful indicators of radiative interactions, and have aided the development of improved techniques in the remote sensing of clouds. This work extends previous theoretical studies to absorbing wavelengths by using both Landsat Thematic Mapper (TM) observations and Monte Carlo (MC) simulations to infer the systematic dependencies of power spectral shape on cloud characteristics, illumination conditions, and wavelength. We show that MC simulations operating on a simple fractal model of horizontally inhomogeneous clouds produce power spectra that qualitatively resemble observed spectra. We also show that the decrease in the spectra power-law exponent seen at intermediate scales (referred to as "roughening") as the Sun becomes more oblique is more pronounced at absorbing wavelengths. An automated procedure designed to detect the small scale break location is unable to find systematic differences between TM Band 4 and Band 7, despite the fact that MC simulations point to systematic differences in horizontal fluxes. The effect of these qualitative characteristics of the spatial spectra on the retrieval of cloud optical properties is examined by comparing power spectra of nadir radiances with power spectra of optical properties retrieved using either traditional Independent Pixel Approximation approaches or modifications based on normalized radiance indices and the inverse Non-local Independent Pixel Approximation. Assuming that the actual cloud properties follow perfect scaling behavior at all scales, we show the improvement of the proposed retrieval modifications.

Oreopoulos, Lazaros↗
NASA NTRS2017

Accounting for the Effects of Surface BRDF on Satellite Cloud and Trace-Gas Retrievals: A New Approach Based on Geometry-Dependent Lambertian-Equivalent Reflectivity Applied to OMI Algorithms

Most satellite nadir ultraviolet and visible cloud, aerosol, and trace-gas algorithms make use of climatological surface reflectivity databases. For example, cloud and NO2 retrievals for the Ozone Monitoring Instrument (OMI) use monthly gridded surface reflectivity climatologies that do not depend upon the observation geometry. In reality, reflection of incoming direct and diffuse solar light from land or ocean surfaces is sensitive to the sun-sensor geometry. This dependence is described by the bidirectional reflectance distribution function (BRDF). To account for the BRDF, we propose to use a new concept of geometry-dependent Lambertian equivalent reflectivity (LER). Implementation within the existing OMI cloud and NO2 retrieval infrastructure requires changes only to the input surface reflectivity database. The geometry-dependent LER is calculated using a vector radiative transfer model with high spatial resolution BRDF information from the Moderate Resolution Imaging Spectroradiometer (MODIS) over land and the Cox-Munk slope distribution over ocean with a contribution from water-leaving radiance. We compare the geometry-dependent and climatological LERs for two wavelengths, 354 and 466 nm, that are used in OMI cloud algorithms to derive cloud fractions. A detailed comparison of the cloud fractions and pressures derived with climatological and geometry-dependent LERs is carried out. Geometry-dependent LER and corresponding retrieved cloud products are then used as inputs to our OMI NO2 algorithm. We find that replacing the climatological OMI-based LERs with geometry-dependent LERs can increase NO2 vertical columns by up to 50% in highly polluted areas; the differences include both BRDF effects and biases between the MODIS and OMI-based surface reflectance data sets. Only minor changes to NO2 columns (within 5 %) are found over unpolluted and overcast areas.

Lambert-equivalent reflectivity↗
DOE OSTI2022

Cluster environment quenches the star formation of low-mass satellite galaxies from the inside-out

ABSTRACT Environment plays a critical role in the star formation history of galaxies. Tidal and hydrodynamical stripping, prominent in cluster environment, can remove the peripheral gas of galaxies and star formation may thus be environmentally suppressed from the outside-in. We revisit the environmental dependence of the radial gradient of specific star formation rate (sSFR) profile. We probe the radial gradient by using the archival spectral indices D4000n and HδA measured from SDSS fibre spectra, to indicate central sSFR and the total sSFR from fitting the spectral energy distribution. Despite the low spatial resolution, the wealth of SDSS data allows to disentangle the dependences on stellar mass, sSFR, and environment. We find that low-mass satellite galaxies in the mass range $9\lt \mathrm{log}\, \mathcal {M}_{\star }/\mathcal {M}_{\odot }\lt 9.8$ on average quench in more inside-out pattern compared to isolated galaxies matched in mass, sSFR, and fibre coverage. This environmental effect is particularly strong for galaxies below the star formation main sequence, and peaks for those in the core of massive clusters where the phase-space diagram reveals clear links between the inside-out quenching and orbital properties. Our results suggest that both tidal and hydrodynamical interactions in cluster environment suppress the star formation of satellites mainly from the inside-out. As accreted gas of low angular momentum from hot gas haloes is an important source for replenishing central gas reservoir, we discuss how gas stripping in clusters may lead to starvation and cause inside-out quenching when the outer star-forming discs are not significantly affected.

79 ASTRONOMY AND ASTROPHYSICS↗