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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

Observations of NGC 604 over six decades in frequency

Maps in the near-infrared, H-alpha and radio continuum have been obtained of NGC 604, the brightest H II region complex in M33, as well as a measurement of the UV spectrum of the ionizing star cluster. The data show that NGC 604 consists of about ten components of typical size 10 pc, embedded in a much larger envelope with dimensions of 225 pc. At least one component is optically almost completely obscured; it is located close to another object that is only detected in the near-infrared and whose nature is uncertain.

Israel, F. P.↗
NASA NTRS1982

Mars: Definition and characterization of global surface units with emphasis on composition

Viking 2 approach maps of red and violet normal albedo are used to define units of similar optical properties for a large area of the Martian surface. Computer processing of these data reveals clusters of albedo values relating to specific, contiguous regions of the planet, with some unit boundaries being of a gradational nature while others are more discrete. A study of the albedo properties of analog materials measured in the laboratory, and of telescopic spectrophotometry of Mars, indicates that both hydroxylated and hematitic oxidized materials exist on the surface. The most probable surface expression of global dust and/or high albedo soils is in the classical bright region, Arabia. Most low albedo features in the region studied have red and violet albedos that are consistent with a dark substrate, such as basalt, coated by, or mixed with, a fine grained alteration product.

Mccord, T. B.↗
NASA NTRS1986

The X-ray surface brightness distribution and spectral properties of six early-type galaxies

Detailed analysis is presented of the Einstein X-ray observations of six early-type galaxies. The results show that effective cooling is probably present in these systems, at least in the innermost regions. Interaction with the surrounding medium has a major effect on the X-ray surface brightness distribution at large radii, at least for galaxies in clusters. The data do not warrant the general assumptions of isothermality and gravitational hydrostatic equilibrium at large radii. Comparison of the X-ray surface brightness profiles with model predictions indicate that 1/r-squared halos with masses of the order of 10 times the stellar masses are required to match the data. The physical model of White and Chevalier (1984) for steady cooling flows in a King law potential with no heavy halo gives a surface brightness distribution that resembles the data if supernovae heating is present.

Trinchieri, G.↗
NASA NTRS1990

Computation and parallel implementation for early vision

The problem of early vision is to transform one or more retinal illuminance images-pixel arrays-to image representations built out of such primitive visual features such as edges, regions, disparities, and clusters. These transformed representations form the input to later vision stages that perform higher level vision tasks including matching and recognition. Researchers developed algorithms for: (1) edge finding in the scale space formulation; (2) correlation methods for computing matches between pairs of images; and (3) clustering of data by neural networks. These algorithms are formulated for parallel implementation of SIMD machines, such as the Massively Parallel Processor, a 128 x 128 array processor with 1024 bits of local memory per processor. For some cases, researchers can show speedups of three orders of magnitude over serial implementations.

Gualtieri, J. Anthony↗
NASA NTRS1989

Decision net, directed graph, and neural net processing of imaging spectrometer data

A decision-net solution involving a novel hierarchical classifier and a set of multiple directed graphs, as well as a neural-net solution, are respectively presented for large-class problem and mixture problem treatments of imaging spectrometer data. The clustering method for hierarchical classifier design, when used with multiple directed graphs, yields an efficient decision net. New directed-graph rules for reducing local maxima as well as the number of perturbations required, and the new starting-node rules for extending the reachability and reducing the search time of the graphs, are noted to yield superior results, as indicated by an illustrative 500-class imaging spectrometer problem.

Casasent, David↗
NASA NTRS2006

AIRS Observations of Deep Convective Clouds

Large thunderstorms can be identified in the AIRS data as areas where the brightness temperature of the 1231 cm-1 atmospheric window channel in non-polar areas is less than 210 K. Each day about 6000 large thunderstorms are identified, almost exclusively within 30 degrees of the equator. Since the size of the AIRS footprint at nadir is 13.5 km, a brightness temperature of less than 210 K indicates that the top of the anvil of the thunderstorm protrudes well into the tropopause. Such objects are commonly referred to as Deep Convective Clouds (DCC). Our interest in DCC was motivated by the question 'Are severe weather events increasing due to global warming'. Each DCC is a severe weather event, although not on the scale of the much less frequent hurricanes, which can be identified in the AIRS data as clusters of several hundred DCC. The number of DCC per day has been fairly stable over the past four years for the mean of the tropical oceans, but a significant increase can be seen day and night in the Atlantic Ocean. The number of DCC per day shows a strong seasonal and latitudinal dependence, with the peak count lagging the solstice of the latitude zone by about 2 months. The most prominent features in brightness temperature spectra of DCC are due to stratospheric CO2, Ozone and Methane. In the channels with weighting functions below the stratosphere the brightness temperature is typically 205 K, with a characteristic 1 to 2.5 K drop between 1000 and 750 cm-1, equivalent to a 2-4 % drop in emissivity. This is likely due to the presence of cirrus (ice) particles. Some of this analysis of DCC can be extended using past and future operational sounders in polar orbit.

temperature↗
NASA NTRS2010

Turbulence and Global Properties of the Solar Wind

The solar wind shows striking characteristics that suggest that it is a turbulent magnetofluid, but the picture is not altogether simple. From the earliest observations, a strong correlation between magnetic fluctuations and plasma velocity fluctuations was noted. The high corrections suggest that the fluctuations are Alfven waves. In addition, the power spectrum of the magnetic fluctuation showed evidence of an inertial range that resembled that seen in fully-developed fluid turbulence. Alfven waves, however, are exact solutions of the equations of incompressible magnetohydrodynamics. Thus, there was a puzzle: how can a magnetofluid consisting of Alfven waves be turbulent? The answer lay in the role of velocity shears in the solar wind that could drive turbulent evolution. Puzzles remain: for example, the power spectrum of the velocity fluctuations is less steep than the slope of the magnetic fluctuations. The plasma in the magnetic tail of Earth's magnetosphere also shows aspects of turbulence, as does the plasma in the dayside magnetosphere near the poles the dayside cusps. Recently, new analyses of high time resolution magnetic field data from Cluster have offered a glimpse of how turbulence is dissipated, thus heating the ambient plasma.

Goldstein, Melvyn L.↗
NASA NTRS2012

New Insight into Short-Wavelength Solar Wind Fluctuations from Vlasov Theory

The nature of solar wind (SW) turbulence below the proton gyroscale is a topic that is being investigated extensively nowadays, both theoretically and observationally. Although recent observations gave evidence of the dominance of kinetic Alfven waves (KAWs) at sub-ion scales with omega < omega(sub ci), other studies suggest that the KAW mode cannot carry the turbulence cascade down to electron scales and that the whistler mode (i.e., omega > omega (sub ci)) is more relevant. Here, we study key properties of the short-wavelength plasma modes under limited, but realistic, SW conditions, Typically Beta(sub i) approx. > Beta (sub e) ∼ 1 and for high oblique angles of propagation 80 deg <= Theta (sub kB) < 90 deg as observed from the Cluster spacecraft data. The linear properties of the plasma modes under these conditions are poorly known, which contrasts with the well-documented cold plasma limit and/or moderate oblique angles of propagation (Theta (sub kB) < 80 deg). Based on linear solutions of the Vlasov kinetic theory, we discuss the relevance of each plasma mode (fast, Bernstein, KAW, whistler) in carrying the energy cascade down to electron scales. We show, in particular, that the shear Alfven mode (known in the magnetohydrodynamic limit) extends at scales kappa rho (sub i) approx. > 1 to frequencies either larger or smaller than omega (sub ci), depending on the anisotropy kappa (parallel )/ kappa(perpendicular). This extension into small scales is more readily called whistler (omega > omega (sub ci)) or KAW (omega < omega (sub ci)) although the mode is essentially the same. This contrasts with the well-accepted idea that the whistler branch always develops as a continuation at high frequencies of the fast magnetosonic mode. We show, furthermore, that the whistler branch is more damped than the KAW one, which makes the latter the more relevant candidate to carry the energy cascade down to electron scales. We discuss how these new findings may facilitate resolution of the controversy concerning the nature of the small-scale turbulence, and we discuss the implications for present and future spacecraft wave measurements in the SW.

waves↗
DOE OSTI2017

BD-CATS-IO

Modern cosmology and plasma physics codes are now capable of simulating trillions of particles on petascale systems. Each timestep output from such simulations is on the order of 10s of TBs. Summarizing and visualizing raw particle data is challenging, and scientists often focus on density structures, whether in the real 3D space, or a high-dimensional phase space. In this work, code was developed for reading 10s of terabytes of data and for writing the resultant cluster IDs for each particle. This code will be part of a highly scalable version of the clustering algorithm DBSCAN, and apply it to the largest datasets produced by state-of-the-art codes. The overall function of BD-CATS (Big Data Clustering at Trillion Particles Scale) is to perform end-to-end analysis: loading the data, dynamic partitioning, computing kdtrees, performing clustering analysis, and storing the results. BD-CATS-IO provides the functionality of loading the data and storing the results.

Byna, Surendra↗
DOE OSTI2013

Automated Detection of Anomalous Shipping Manifests to Identify Illicit Trade

We describe an approach to analyzing trade data which uses clustering to detect similarities across shipping manifest records, classification to evaluate clustering results and categorize new unseen shipping data records, and visual analytics to provide to support situation awareness in dynamic decision making to monitor and warn against the movement of radiological threat materials through search, analysis and forecasting capabilities. The evaluation of clustering results through classification and systematic inspection of the clusters show the clusters have strong semantic cohesion and offer novel ways to detect transactions related to nuclear smuggling.

classification↗
DOE OSTI2017

Weak-lensing mass calibration of redMaPPer galaxy clusters in Dark Energy Survey Science Verification data

We use weak-lensing shear measurements to determine the mean mass of optically selected galaxy clusters in Dark Energy Survey Science Verification data. In a blinded analysis, we split the sample of more than 8,000 redMaPPer clusters into 15 subsets, spanning ranges in the richness parameter $5 \leq \lambda \leq 180$ and redshift $0.2 \leq z \leq 0.8$, and fit the averaged mass density contrast profiles with a model that accounts for seven distinct sources of systematic uncertainty: shear measurement and photometric redshift errors; cluster-member contamination; miscentering; deviations from the NFW halo profile; halo triaxiality; and line-of-sight projections. We combine the inferred cluster masses to estimate the joint scaling relation between mass, richness and redshift, $\mathcal{M}(\lambda,z) \varpropto M_0 \lambda^{F} (1+z)^{G}$. We find $M_0 \equiv \langle M_{200\mathrm{m}}\,|\,\lambda=30,z=0.5\rangle=\left[ 2.35 \pm 0.22\ \rm{(stat)} \pm 0.12\ \rm{(sys)} \right] \cdot 10^{14}\ M_\odot$, with $F = 1.12\,\pm\,0.20\ \rm{(stat)}\, \pm\, 0.06\ \rm{(sys)}$ and $G = 0.18\,\pm\, 0.75\ \rm{(stat)}\, \pm\, 0.24\ \rm{(sys)}$. The amplitude of the mass-richness relation is in excellent agreement with the weak-lensing calibration of redMaPPer clusters in SDSS by Simet et al. (2016) and with the Saro et al. (2015) calibration based on abundance matching of SPT-detected clusters. Our results extend the redshift range over which the mass-richness relation of redMaPPer clusters has been calibrated with weak lensing from $z\leq 0.3$ to $z\leq0.8$. Calibration uncertainties of shear measurements and photometric redshift estimates dominate our systematic error budget and require substantial improvements for forthcoming studies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Transfer Learning Trained LSTM Models for Household Load Profile Forecasting

Grid edge renewable energy resources, such as rooftop solar photovoltaics, closely interact with consumer load profiles. Therefore, forecasting future electricity demand, ideally at the individual household level, is indispensable. In this paper, we present a transfer learning enhanced household load profile forecasting method. First, we tune a long short-term memory forecasting model to perform day-ahead prediction of household electricity load profiles. Then we improve these individualized models using transfer learning, and we use k-means clustering to create optimal source data sets. We find average improvements of 4.38% (largest improvement of 10.71%) when the entire data set was used to train the source model and 2.45% (largest improvement of 11.57%) in the mean absolute error when households were first clustered and used to train separate source models for each cluster. We find that transfer learning with clustered data can effectively boost the forecasting performance of the LSTM models. We use realistic household power measurements for 148 real residential households in Austin, Texas.

deep learning↗
DOE OSTI2017

Formation and study of p–i–n structures based on two-phase hydrogenated silicon with a germanium layer in the i-type region

Four pairs of p–i–n structures based on polymorphous Si:H (pm-Si:H) are fabricated by the method of plasma-enhanced chemical vapor deposition. The structures in each pair are grown on the same substrate so that one of them does not contain Ge in the i-type layer while the other structure contains Ge deposited by molecular-beam epitaxy as a layer with a thickness of 10 nm. The pair differ from one another in terms of the substrate temperature during Ge deposition; these temperatures are 300, 350, 400, and 450°C. The data of electron microscopy show that the structures formed at 300°C contain Ge nanocrystals (nc-Ge) nucleated at nanocrystalline inclusions at the pm-Si:H surface. The nc-Ge concentration increases as the temperature is raised. The study of the current–voltage characteristics show that the presence of Ge in the i-type layer decreases the density of the short-circuit current in p–i–n structures when they are used as solar cells, whereas these layers give rise to an increase in current at a reverse bias under illumination. The obtained results are consistent with known data for structures with Ge clusters in Si; according to these data, Ge clusters increase the coefficient of light absorption but they also increase the rate of charge-carrier recombination.

36 MATERIALS SCIENCE↗
NASA NTRS1973

Cluster Seeking Techniques in Pattern Recognition

A cluster seeking technique is defined as a method of dividing data into subsets, called clusters. These clusters contain data points that are similar to each other and different from the elements of other clusters. Various cluster seeking techniques were broken down into seven categories: (1) probabilistic, (2) signal detection, (3) clustering, (4) clumping, (5) eigenvalue, (6) minimal mode seeking, and (7) miscellaneous. Each category is described and one or more algorithms of that type are presented.

Barr, B. J.↗
NASA NTRS1981

Verification of land cover maps from LANDSAT data

The application of cluster sampling to verifying the accuracy of maps derived from digital data is discussed. Variants of the cluster sampling technique were used in large scale assessments for areas in excess of one million hectares. Three types of classification errors are possible: commission errors, omission errors, and overall error classification. Commission errors for a particular cover type occur when pixels are classified as that cover type but are found to be some other cover type when the field is checked. Omission errors occur when pixels of fields already visited and known to be of a particular cover type, are classified as some other cover type. Overall error is the proportion of pixels incorrectly classified, without regard to omission or commission. Since the classified image represents the sampling frame, sampling for accuracy assessment was designed to estimate commission error. However, the sample can also provide useful estimates of omission and overall error. Assessment for Alaska, Arizona, and Oklahoma are presented.

Linden, D. S.↗