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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 37 records · Page 2

Enhanced star formation and infrared emission in the centers of galaxies

The IR properties of galaxies with star-formation-dominated central IR luminosity are discussed, summarizing the results of recent observational investigations. Consideration is given to the constituents of star-forming galaxies (dust in emission, photoionized gas, and shock-heated gas), the IR morphology (IR luminosity and young stars and NIR mapping to see beyond dust), the relationship between IR and radio emission, M82 as a typical object, and the implications of the observations for theoretical models of star formation. It is concluded that star formation in these galaxies will exhaust the available gas supply in much less than a Hubble time. Diagrams, graphs, sample images, and tables of numerical data are provided.

Telesco, Charles M.↗

A high-fidelity N-body ephemeris generator for satellites in Earth orbit

A program is currently used for mission planning called the Analytic Satellite Ephemeris Program (ASEP), which produces projected data for orbits that remain fairly close to Earth. Lunar and solar perturbations are taken into account in another program called GRAVE. This project is a revision of GRAVE which incorporates more flexible means of input for initial data, provides additional kinds of output information, and makes use of structured programming techniques to make the program more understandable and reliable. The computer program ORBIT was tested against tracking data for the first 313 days of operation of the CRRES satellite. A sample graph is given comparing the semi-major axis calculated by the program with the values supplied by NORAD. When calculated for points at which CRRES passes through the ascending node, the argument of perigee, the right ascension of the ascending node, and the mean anomaly all stay within about a degree of the corresponding values from NORAD; the inclination of the orbital plane is much closer. The program value of the eccentricity is in error by no more than 0.0002.

Simmons, David R.↗

Explore Spatio‐Temporal Learning of Large Sample Hydrology Using Graph Neural Networks

Abstract Streamflow forecasting over gauged and ungauged basins play a vital role in water resources planning, especially under the changing climate. Increased availability of large sample hydrology data sets, together with recent advances in deep learning techniques, has presented new opportunities to explore temporal and spatial patterns in hydrological signatures for improving streamflow forecasting. The purpose of this study is to adapt and benchmark several state‐of‐the‐art graph neural network (GNN) architectures, including ChebNet, Graph Convolutional Network (GCN), and GraphWaveNet, for end‐to‐end graph learning. We explicitly represent river basins as nodes in a graph, learn the spatiotemporal nodal dependencies, and then use the learned relations to predict streamflow simultaneously across all nodes in the graph. The efficacy of the developed GNN models is investigated using the Catchment Attributes and MEteorology for Large‐sample Studies (CAMELS) data set under two settings, fixed graph topology (transductive learning), and variable graph topology (inductive learning), with the latter applicable to prediction in ungauged basins (PUB). Results indicate that GNNs are generally robust and computationally efficient, achieving similar or better performance than a baseline model trained using the long short‐term memory (LSTM) network. Further analyses are conducted to interpret the graph learning process at the edge and node levels and to investigate the effect of different model configurations. We conclude that graph learning constitutes a viable machine learning‐based method for aggregating spatiotemporal information from a multitude of sources for streamflow forecasting

Sun, Alexander Y.↗

Phasor Measurement Units Optimal Placement and Performance Limits for Fault Localization

Here, the performance limits of faults localization are investigated using synchrophasor data. The focus is on a non-trivial operating regime where the number of Phasor Measurement Unit (PMU) sensors available is insufficient to have full observability of the grid state. Proposed analysis uses the Kullback Leibler (KL) divergence between the distributions corresponding to different fault location hypotheses associated with the observation model. This analysis shows that the most likely locations are concentrated in clusters of buses more tightly connected to the actual fault site akin to graph communities. Consequently, a PMU placement strategy is derived that achieves a near-optimal resolution for localizing faults for a given number of sensors. The problem is also analyzed from the perspective of sampling a graph signal, and how the placement of the PMUs i.e. the spatial sampling pattern and the topological characteristic of the grid affect the ability to successfully localize faults. To highlight the superior performance of presented fault localization and placement algorithms, the proposed strategy is applied to a modified IEEE 34, IEEE-123 bus test cases and to data from a real distribution grid. Additionally, the detection of cyber-physical attacks is also examined where PMU data and relevant Supervisory Control and Data Acquisition (SCADA) network traffic information are compared to determine if a network breach has affected the integrity of the system information and/or operations.

42 ENGINEERING↗

Hyperspectral Image Classification Using Graph Clustering Methods

Hyperspectral imagery is a challenging modality due to the dimension of the pixels which can range from hundreds to over a thousand frequencies depending on the sensor. Most methods in the literature reduce the dimension of the data using a method such as principal component analysis, however this procedure can lose information. More recently methods have been developed to address classification of large datasets in high dimensions. This paper presents two classes of graph-based classification methods for hyperspectral imagery. Using the full dimensionality of the data, we consider a similarity graph based on pairwise comparisons of pixels. The graph is segmented using a pseudospectral algorithm for graph clustering that requires information about the eigenfunctions of the graph Laplacian but does not require computation of the full graph. We develop a parallel version of the Nyström extension method to randomly sample the graph to construct a low rank approximation of the graph Laplacian. With at most a few hundred eigenfunctions, we can implement the clustering method designed to solve a variational problem for a graph-cut-based semi-supervised or unsupervised classification problem. We implement OpenMP directive-based parallelism in our algorithms and show performance improvement and strong, almost ideal, scaling behavior. The method can handle very large datasets including a video sequence with over a million pixels, and the problem of segmenting a data set into a pre-determined number of classes.

47 OTHER INSTRUMENTATION↗

Enhanced Sampling of Crystal Nucleation with Graph Representation Learnt Variables

In this study, we present a graph neural network-based learning approach using an autoencoder setup to derive low-dimensional variables from features observed in experimental crystal structures. These variables are then biased in enhanced sampling to observe state-to-state transitions and reliable thermodynamic weights. Our approach uses simple convolution and pooling methods. To verify the effectiveness of our protocol, we examined the nucleation of various allotropes and polymorphs of iron and glycine from their molten states. Our graph latent variables when biased in well-tempered metadynamics consistently show transitions between states and achieve accurate free energy calculations in agreement with experiments, both of which are indicators of dependable sampling. This underscores the strength and promise of our graph neural net variables for improved sampling. Furthermore, the protocol shown here should be applicable for other systems and with other sampling methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Accurate Characterization of Real Networks from Inaccurate Measurements

Our nation's dependence on information networks makes it vital to anticipate disruptions or find weaknesses in these networks. But networks like the Internet are vast, distributed, and there is no mechanism to completely collect their structure. We are restricted to specific data collection schemes (like traceroute samples from router interfaces) that examine tiny portions of such a network. It has been empirically documented and theoretically proven that these measurements have significant biases, and direct inferences from them will be wrong. But these data collection mechanisms have limited flexibility and cannot be easily modified. Moreover, in many applications there are limits on how much data can be collected. How do we make accurate inferences of network properties with biased and limited measurements? The general problem this report deals with is how to work with incompletely observed networks. We will present several different approaches to this problem. First we will present an approach to estimate the degree distribution of a graph by sampling only a small portion of the vertices. This algorithm provides provably accurate results with sublinear samples. An alternative approach would be to try to enhance the information in the by selective collecting new information by probing for neighbors of a vertex or presence of individual edges. A different setting for working with incomplete arises when we have full access to local information, but do not have any global version of the graph. Can we still identify critical nodes in such a graph? We present an approach to identify such nodes efficiently. Finally, how can we put these ideas together to identify the structure of a network? We present an approach that can complement the existing approaches for network mapping. We start with an estimate of network structure based on existing network mapping methods. Then we find a critical router in the network, use the traffic through this network to selectively collect new data to enhance our prediction.

97 MATHEMATICS AND COMPUTING↗

Quickest Detection for Changes in Maximal kNN Coherence of Random Matrices

This paper addresses the problem of quickest detection of a change in the maximal coherence between columns of a n p random matrix based on a sequence of matrix observations having a single unknown change point. The random matrix is assumed to have identically distributed rows and the maximal coherence is defined as the largest of the ( 2 p) correlation coefficients associated with any row. Likewise the κ nearest neighbor (kNN) coherence is defined as the κ-th largest of these correlation coefficients. The forms of the preand postchange distributions of the observed matrices are assumed to belong to the family of elliptically contoured densities with sparse dispersion matrices but are otherwise unknown. A nonparametric stopping rule is proposed that is based on the maximal kNN sample coherence between columns of each observed random matrix. This is a summary statistic that is related to a test of existence of a hub vertex in a sample correlation graph having degree at least κ. Performance bounds on the delay and false alarm performance of the proposed stopping rule are obtained in the purely high-dimensional regime where p → ∞ and n is fixed. When the prechange dispersion matrix is diagonal it is shown that, among all functions of the proposed summary statistic, the proposed stopping rule is asymptotically optimal under a minimax quickest change detection model as the stopping threshold approaches infinity. Furthermore, the theory developed also applies to sequential hypothesis testing and fixed sample size tests.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Hydrogen in disordered titania: connecting local chemistry, structure, and stoichiometry through accelerated exploration

Hydrogen incorporation in native surface oxides of metal alloys often controls the onset of metal hydriding, with implications for materials corrosion and hydrogen storage. A key representative example is titania, which forms as a passivating layer on a variety of titanium alloys for structural and functional applications. These oxides tend to be structurally diverse, featuring polymorphic phases, grain boundaries, and amorphous regions that generate a disparate set of unique local environments for hydrogen. Here, we introduce a workflow that can efficiently and accurately navigate this complexity. First, a machine learning force field, trained on ab initio molecular dynamics simulations, was used to generate amorphous configurations. Density functional theory calculations were then performed on these structures to identify local oxygen environments, which were compared against experimental observations. Second, to classify subtle differences across the disordered configuration space, we employ a graph-based sampling procedure. Finally, local hydrogen binding energies and hopping kinetics are computed using exhaustive density functional theory calculations on representative configurations. Here, we leverage this methodology to show that hydrogen binding energetics are described by local oxygen coordination, which in turn is affected by stoichiometry, and form the basis of hopping kinetics and diffusion. Together these results imply that hydrogen incorporation and transport in TiO x can be tailored through compositional engineering, with implications for improving the performance and durability of titanium-derived alloys in hydrogen environments.

36 MATERIALS SCIENCE↗

Direct measurement of skin friction with a new instrument

The design and performance of a small belt-type skin-friction gage to measure wall shear-stress coefficients in wind-tunnel testing are described, summarizing the report of Vakili and Wu (1982). The sensor employs a flexible belt of variable surface characteristics; this belt, wrapped tightly around two cylinders mounted on frictionless flexures, is equipped with strain gages to estimate the deflection of the belt by the flow. An alternative approach uses IR illumination, optical fibers, and a photosensitive transistor, permitting direct measurement of the belt deflection. Drawings, diagrams, and graphs of sample data are provided.

Vakili, A. D.↗

Chromospheres and transition regions

The properties of stellar chromospheres are examined, summarizing the results of observations obtained with the IUE satellite since its launch in 1978. Numerous sample spectra, graphs, and diagrams are presented and analyzed in detail. Consideration is given to 120-320-nm spectroscopy of main-sequence stars, giants, and supergiants; the global properties of main-sequence and post-main-sequence chromospheres; dynamic phenomena and structures (systematic flows, atmospheric inhomogeneities, and intrinsic variability); and structural and energy-balance modeling. A number of outstanding problems are listed, and the potential value of data from the Hubble Space Telescope and the Lyman FUV Spectroscopic Explorer in solving them is indicated.

Jordan, Carole↗

Pre-main sequence stars

Observations of premain-sequence (PMS) stars obtained with the IUE satellite since its launch in 1978 are reviewed. The major types of PMS stars are listed and briefly characterized: T Tau stars, Herbig Ae stars, FU Ori objects, weak-emission PMS stars, and Herbig-Haro objects. The IUE data for a catalog of PMS objects are then presented in a table, graphs, and sample spectra and described in detail. Topics addressed include general spectral profiles, the Mg II h and k lines, the FUV emission lines, the UV continuum, and the impact of the IUE on astrophysical models of PMS stars. The need for observations with higher spectral resolution and much greater sensitivity is indicated, and the ability of the Hubble Space Telescope High-Resolution Spectrograph and the proposed NASA/ESA Lyman FUV Spectroscopic Explorer to meet these requirements is considered.

Imhoff, Catherine L.↗

Boundary force method analyses of notched plates with cracks

The boundary-force method developed by Tan (1985) and Tan et al. (1986) is extended to two-dimensional mixed boundary-value problems in fracture mechanics. The formulation is explained in detail, and results are presented in tables and graphs for sample problems involving an edge crack emanating from a semielliptical and a semicircular notch and an edge crack emanating from a V notch. The accuracy of the approach is demonstrated by comparisons with collocation solutions for an edge-clamped center-cracked tension specimen.

Newman, J. C., Jr.↗

A postencounter view of comets

Ground-based and space observations of Comet Halley during its 1986 perihelion passage are reviewed, with an emphasis on their implications for theoretical models. Consideration is given to the shape, surface morphology, and composition of the comet nucleus; the shape, dynamics, and composition of the dust tail; neutral and ionic gas species in the head and plasma tail; and the comet/solar-wind interaction. Extensive diagrams, graphs, and sample images are provided, and the potential value of the new kinds of data to be obtained with the NASA Comet-Rendezvous/Asteroid-Flyby spacecraft is discussed.

Mendis, D. A.↗