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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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DOE OSTI2016

Iterative Strategies for Aftershock Classification in Automatic Seismic Processing Pipelines

We report aftershock sequences following very large earthquakes present enormous challenges to near-real-time generation of seismic bulletins. The increase in analyst resources needed to relocate an inflated number of events is compounded by failures of phase-association algorithms and a significant deterioration in the quality of underlying, fully automatic event bulletins. Current processing pipelines were designed a generation ago, and, due to computational limitations of the time, are usually limited to single passes over the raw data. With current processing capability, multiple passes over the data are feasible. Processing the raw data at each station currently generates parametric data streams that are then scanned by a phase-association algorithm to form event hypotheses. We consider the scenario in which a large earthquake has occurred and propose to define a region of likely aftershock activity in which events are detected and accurately located, using a separate specially targeted semiautomatic process. This effort may focus on so-called pattern detectors, but here we demonstrate a more general grid-search algorithm that may cover wider source regions without requiring waveform similarity. Given many well-located aftershocks within our source region, we may remove all associated phases from the original detection lists prior to a new iteration of the phase-association algorithm. We provide a proof-of-concept example for the 2015 Gorkha sequence, Nepal, recorded on seismic arrays of the International Monitoring System. Even with very conservative conditions for defining event hypotheses within the aftershock source region, we can automatically remove about half of the original detections that could have been generated by Nepal earthquakes and reduce the likelihood of false associations and spurious event hypotheses. Lastly, further reductions in the number of detections in the parametric data streams are likely, using correlation and subspace detectors and/or empirical matched field processing.

58 GEOSCIENCES↗

NASA NTRS1979

A survey of airborne radar systems for deployment on a High Altitude Powered Platform (HAPP)

A survey was conducted to find out the system characteristics of commercially available and unclassified military radars suitable for deployment on a stationary platform. A total of ten domestic and eight foreign manufacturers of the radar systems were identified. Questionnaires were sent to manufacturers requesting information concerning the system characteristics: frequency, power used, weight, volume, power radiated, antenna pattern, resolution, display capabilities, pulse repetition frequency, and sensitivity. A literature search was also made to gather the system characteristics information. Results of the survey are documented and comparisons are made among available radar systems.

Choudhury, B. J.↗

NASA NTRS1988

How to cluster in parallel with neural networks

Partitioning a set of N patterns in a d-dimensional metric space into K clusters - in a way that those in a given cluster are more similar to each other than the rest - is a problem of interest in astrophysics, image analysis and other fields. As there are approximately K(N)/K (factorial) possible ways of partitioning the patterns among K clusters, finding the best solution is beyond exhaustive search when N is large. Researchers show that this problem can be formulated as an optimization problem for which very good, but not necessarily optimal solutions can be found by using a neural network. To do this the network must start from many randomly selected initial states. The network is simulated on the MPP (a 128 x 128 SIMD array machine), where researchers use the massive parallelism not only in solving the differential equations that govern the evolution of the network, but also by starting the network from many initial states at once, thus obtaining many solutions in one run. Researchers obtain speedups of two to three orders of magnitude over serial implementations and the promise through Analog VLSI implementations of speedups comensurate with human perceptual abilities.

Kamgar-Parsi, Behzad↗

DOE OSTI2020

Traversing large graphs on GPUs with unified memory

Due to the limited capacity of GPU memory, the majority of prior work on graph applications on GPUs has been restricted to graphs of modest sizes that fit in memory. Recent hardware and software advances make it possible to address much larger host memory transparently as a part of a feature known as unified virtual memory. While accessing host memory over an interconnect is understandably slower, the problem space has not been sufficiently explored in the context of a challenging workload with low computational intensity and an irregular data access pattern such as graph traversal. We analyse the performance of breadth first search (BFS) for several large graphs in the context of unified memory and identify the key factors that contribute to slowdowns. Next, we propose a lightweight offline graph reordering algorithm, HALO (Harmonic Locality Ordering), that can be used as a pre-processing step for static graphs. HALO yields speedups of 1.5x-1.9x over baseline in subsequent traversals. Our method specifically aims to cover large directed real world graphs in addition to undirected graphs whereas prior methods only account for the latter. Additionally, we demonstrate ties between the locality ordering problem and graph compression and show that prior methods from graph compression such as recursive graph bisection can be suitably adapted to this problem.

Gera, Prasun↗

DOE OSTI2016

Graph 500 on OpenSHMEM: Using a Practical Survey of Past Work to Motivate Novel Algorithmic Developments

Graph500 [14] is an effort to offer a standardized benchmark across large-scale distributed platforms which captures the behavior of common communicationbound graph algorithms. Graph500 differs from other large-scale benchmarking efforts (such as HPL [6] or HPGMG [7]) primarily in the irregularity of its computation and data access patterns. The core computational kernel of Graph500 is a breadth-first search (BFS) implemented on an undirected graph. The output of Graph500 is a spanning tree of the input graph, usually represented by a predecessor mapping for every node in the graph. The Graph500 benchmark defines several pre-defined input sizes for implementers to test against. This report summarizes investigation into implementing the Graph500 benchmark on OpenSHMEM, and focuses on first building a strong and practical understanding of the strengths and limitations of past work before proposing and developing novel extensions.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

A SEARCH FOR SPECTRAL HYSTERESIS AND ENERGY-DEPENDENT TIME LAGS FROM X-RAY AND TeV GAMMA-RAY OBSERVATIONS OF Mrk 421

Blazars are variable emitters across all wavelengths over a wide range of timescales, from months down to minutes. It is therefore essential to observe blazars simultaneously at different wavelengths, especially in the X-ray and gamma-ray bands, where the broadband spectral energy distributions usually peak. In this work, we report on three “target-of-opportunity” observations of Mrk 421, one of the brightest TeV blazars, triggered by a strong flaring event at TeV energies in 2014. These observations feature long, continuous, and simultaneous exposures with XMM-Newton (covering the X-ray and optical/ultraviolet bands) and VERITAS (covering the TeV gamma-ray band), along with contemporaneous observations from other gamma-ray facilities (MAGIC and Fermi -Large Area Telescope) and a number of radio and optical facilities. Although neither rapid flares nor significant X-ray/TeV correlation are detected, these observations reveal subtle changes in the X-ray spectrum of the source over the course of a few days. We search the simultaneous X-ray and TeV data for spectral hysteresis patterns and time delays, which could provide insight into the emission mechanisms and the source properties (e.g., the radius of the emitting region, the strength of the magnetic field, and related timescales). The observed broadband spectra are consistent with a one-zone synchrotron self-Compton model. We find that the power spectral density distribution at ≳4 × 10{sup −4} Hz from the X-ray data can be described by a power-law model with an index value between 1.2 and 1.8, and do not find evidence for a steepening of the power spectral index (often associated with a characteristic length scale) compared to the previously reported values at lower frequencies.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1979

Satellite antenna technology. Citations from the International Aerospace Abstracts data base

This bibliography of citations to articles from the international literature concerns satellite antenna technology. Topics stressed are antenna design studies dealing with antenna radiation patterns and antenna arrays. This bibliography excludes articles cited in the published searches titled Communications Satellite Technology. The bibliography contains 213 citations.

Zollars, G. F.↗

NASA NTRS2007

NASA Tech Briefs, March 2007

Topics include: Advanced Systems for Monitoring Underwater Sounds; Wireless Data-Acquisition System for Testing Rocket Engines; Processing Raw HST Data With Up-to-Date Calibration Data; Mobile Collection and Automated Interpretation of EEG Data; System for Secure Integration of Aviation Data; Servomotor and Controller Having Large Dynamic Range; Digital Multicasting of Multiple Audio Streams; Translator for Optimizing Fluid-Handling Components; AIRSAR Web-Based Data Processing; Pattern Matcher for Trees Constructed From Lists; Reducing a Knowledge-Base Search Space When Data Are Missing; Ground-Based Correction of Remote-Sensing Spectral Imagery; State-Chart Autocoder; Pointing History Engine for the Spitzer Space Telescope; Low-Friction, High-Stiffness Joint for Uniaxial Load Cell; Magnet-Based System for Docking of Miniature Spacecraft; Electromechanically Actuated Valve for Controlling Flow Rate; Plumbing Fixture for a Microfluidic Cartridge; Camera Mount for a Head-Up Display; Core-Cutoff Tool; Recirculation of Laser Power in an Atomic Fountain; Simplified Generation of High-Angular-Momentum Light Beams; Imaging Spectrometer on a Chip; Interferometric Quantum-Nondemolition Single-Photon Detectors; Ring-Down Spectroscopy for Characterizing a CW Raman Laser; Complex Type-II Interband Cascade MQW Photodetectors; Single-Point Access to Data Distributed on Many Processors; Estimating Dust and Water Ice Content of the Martian Atmosphere From THEMIS Data; Computing a Stability Spectrum by Use of the HHT; Theoretical Studies of Routes to Synthesis of Tetrahedral N4; Estimation Filter for Alignment of the Spitzer Space Telescope; Antenna for Measuring Electric Fields Within the Inner Heliosphere; Improved High-Voltage Gas Isolator for Ion Thruster; and Hybrid Mobile Communication Networks for Planetary Exploration.

Source record↗

NASA NTRS2008

Exozodiacal Dust and Direct Imaging of Extrasolar Planets

Direct imaging of extrasolar planets means contending with dust from extrasolar asteroids and comets. This 'exozodiacal dust' creates a structured background light that can easily outshine the light from an exoEarth and confuse a planet-search mission like TPF or TOPS. But exozodiacal dust can be both friend and foe: planets can stir dust clouds into patterns that reveal the presence of the planet and constrain its mass and orbit. I'll describe some recent research on this topic: 3-D dynamical models of dust clouds with planets and searches for exozodiacal dust with the Keck Interferometer. The author also offers a prediction for the typical zodiacal dust background found around solar analogs, based on seafloor sediment data.

Kuchner, Marc↗

DOE OSTI2018

Automated assignment of rotational spectra using artificial neural networks

Here, a typical broadband rotational spectrum may contain several thousand observable transitions, spanning many species. While these spectra often encode troves of chemical information, identifying and assigning the individual spectra can be challenging. Traditional approaches typically involve visually identifying a pattern. A more modern approach is to apply an automated fitting routine. In this approach, combinations of 3 transitions are searched by trial and error, to fit the A , B , and C rotational constants in a Watson-type Hamiltonian. In this work, we develop an alternative approach-to utilize machine learning to train a computer to recognize the patterns inherent in rotational spectra. Broadband high-resolution rotational spectra are perhaps uniquely suited for pattern recognition, assignment, and species identification using machine learning. Repeating patterns of transition frequencies and intensities are now routinely recorded in broadband chirped-pulse Fourier transform microwave experiments in which both the number of resolution elements and the dynamic range surpass 10 4 . At the same time, these high-resolution spectra are extremely sensitive to molecular geometry with each polar species having a unique rotational spectrum. Here we train the feed forward neural network on thousands of rotational spectra that we calculate, using the rules of quantum mechanics, from randomly generated sets of rotational constants and other Hamiltonian parameters. Reasonable physical constraints are applied to these parameter sets, yet they need not belong to existing species. A trained neural network presented with a spectrum identifies its type (e.g., linear molecule, symmetric top, or asymmetric top) and infers the corresponding Hamiltonian parameters (rotational constants, distortion, and hyperfine constants). The classification and prediction times, about 160 μs and 50 μs, respectively, seem independent of the spectral complexity or the number of molecular parameters. We describe how the network works, provide benchmarking results, and discuss future directions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Dual phase patterning during a congruent grain boundary phase transition in elemental copper

The phase behavior of grain boundaries can have a strong influence on interfacial properties. Little is known about the emergence of grain boundary phases in elemental metal systems and how they transform. Here, we observe the nanoscale patterning of a grain boundary by two alternating grain boundary phases with distinct atomic structures in elemental copper by atomic resolution imaging. The same grain boundary phases are found by computational grain boundary structure search indicating a first-order transformation. Finite temperature atomistic simulations reveal a congruent, diffusionless transition between these phases under ambient pressure. The patterning of the grain boundary at room temperature is dominated by the grain boundary phase junctions separating the phase segments. Our analysis suggests that the reduced mobility of the phase junctions at low temperatures kinetically limits the transformation, but repulsive elastic interactions between them and disconnections could additionally stabilize the pattern formation.

36 MATERIALS SCIENCE↗

NASA NTRS1998

A Search for Giant Convection Cells on the Sun

Giant convection cells (slow, long-lived cellular flows in the Sun's convection zone with typical diameters greater than about 100,000 km) have been the subject of many searches over the last 30 years. If such flows exist, they should play a key role in structuring the Sun's large scale magnetic field and in driving the large scale axisymmetric flows: the differential rotation and meridional circulation. Detailed observations of the flows in these cells may also allow us to better predict future magnetic field configurations and the solar activity associated with them. Line-of-sight velocity data from the Michelson Doppler Interferometer on the ESA/NASA Solar and Heliospheric Observatory provides us with new opportunities to search for giant cells. This data is free of any atmospheric distortion and has been obtained continuously without any day/night gaps for more than two months at a time. These two-month datasets are important because giant cells are expected to have lifetimes somewhat longer that the Sun's 27 day rotation period. Any reappearance of a flow pattern after 27 days would be an important confirmation of the existence of these cells. The approach taken in this search is to separate the giant cell velocity signal from the other, stronger velocity signals by using a spherical harmonic representation of the spatial structures and a fourier decomposition of the temporal behavior. Any giant cell signal should be characterized by low spatial wavenumbers with temporal frequencies appropriate to the solar rotation of these patterns.

Hathaway, David H.↗

DOE OSTI2024

The R -process Alliance: Fifth Data Release from the Search for R -process-enhanced Metal-poor Stars in the Galactic Halo with the GTC

Understanding the abundance pattern of metal-poor stars and the production of heavy elements through various nucleosynthesis processes offers crucial insights into the chemical evolution of the Milky Way, revealing primary sites and major sources of rapid neutron-capture process (r-process) material in the Universe. In this fifth data release from the R-Process Alliance (RPA), we present the detailed chemical abundances of 41 faint (down to V = 15.8) and extremely metal-poor (down to [Fe/H] = -3.3) halo stars selected from the RPA. We obtained high-resolution spectra for these objects with the HORuS spectrograph on the Gran Telescopio Canarias. We measure the abundances of light, α, Fe-peak, and neutron-capture elements. We report the discovery of five carbon-enhanced metal-poor, one limited-r, three r-I, and four r-II stars, and six Mg-poor stars. We also identify one star of a possible globular cluster origin at an extremely low metallicity at [Fe/H] = -3.0. This adds to the growing evidence of a lower-limit metallicity floor for globular cluster abundances. We use the abundances of Fe-peak elements and the α-elements to investigate the contributions from different nucleosynthesis channels in the progenitor supernovae. We find the distribution of [Mg/Eu] as a function of [Fe/H] to have different enrichment levels, indicating different possible pathways and sites of their production. We also reveal differences in the trends of the neutron-capture element abundances of Sr, Ba, and Eu of various r-I and r-II stars from the RPA data releases, which provide constraints on their nucleosynthesis sites and subsequent evolution.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1963

SOME GEOLOGIC PROBLEMS OF MARS

Geological and geophysical knowledge and uncertainties concerning the surface and body of Mars are briefly discussed and evaluated. The large number of uncertainties in present data precludes an adequate model of Mars; reliable data are needed. Accurate values for the figure of the planet and the radii, seismic evidence of internal structure, and measurements of the outward heat flux across the surface are important and necessary for a satisfactory description of the body of Mars. Two types of television coverage from an orbiter are required to describe surface features. One type is needed to delineate gross topographic features, such as extensive highlands or basins, with resolution of about one kilometer. The second type is needed to search for evidence of orogenic and volcanic activity, areas of groundwater accumulation, sand dune patterns, etc., with resolution of about 10 meters. Some geological inferences which can be drawn from available photographic and photometric data concerning topography, areas of water accumulation, and biological activity are discussed. The priority of scientific geologic experiments is presented and some present instrumentation capabilities and deficiencies are listed.

Mars surface↗

NASA NTRS1988

Autonomous star identification for spacecraft attitude control

Research is being conducted to enhance the Advanced Star and Target Reference Optical Scanner (ASTROS I) so that it will be able to automatically track a star and recognize the star's pointing position. Previously developed field identification algorithms, coded on a flight-scale computer, are linked to a breadboard ASTROS star tracker to calculate pointing vectors in real time. A simple graph-searching algorithm was used in an initial study to find stellar positions based on pattern recognition. Results from this study and plans for further development are presented.

Rappaport, Barry↗

NASA NTRS2013

Costs of Limiting Route Optimization to Published Waypoints in the Traffic Aware Planner

The Traffic Aware Planner (TAP) is an airborne advisory tool that generates optimized, traffic-avoiding routes to support the aircraft crew in making strategic reroute requests to Air Traffic Control (ATC). TAP is derived from a research-prototype self-separation tool, the Autonomous Operations Planner (AOP), in which optimized route modifications that avoid conflicts with traffic and weather, using waypoints at explicit latitudes and longitudes (a technique supported by self-separation concepts), are generated by maneuver patterns applied to the existing route. For use in current-day operations in which trajectory changes must be requested from ATC via voice communication, TAP produces optimized routes described by advisories that use only published waypoints prior to a reconnection waypoint on the existing route. We describe how the relevant algorithms of AOP have been modified to implement this requirement. The modifications include techniques for finding appropriate published waypoints in a maneuver pattern and a method for combining the genetic algorithm of AOP with an exhaustive search of certain types of advisory. We demonstrate methods to investigate the increased computation required by these techniques and to estimate other costs (measured in terms such as time to destination and fuel burned) that may be incurred when only published waypoints are used.

TASAR↗

DOE OSTI2024

Identification of Distorted Gamma-Ray Signature Patterns Using Digital Filtering and Auto-Associative Memory Implemented with a Hopfield Neural Network

The detection and identification of radioactive sources in search applications involve analyzing passive gamma-ray emissions from high-level radioactive materials. This process uses a mobile detector-spectrometer in a complex field test environment. Recently, the use of artificial intelligence for gamma-ray spectrum analysis has shown promising results. However, challenges persist in identifying isotopic signatures from spectral measurements that may be distorted due to source shielding, random variations in natural radioactive background, or insufficient measurement time to obtain clear spectral lines. Here, this paper presents a novel intelligent signature recognition method that combines digital filtering techniques with an artificial Hopfield Neural Network (HNN). The HNN leverages auto-associative memory to store training sample patterns and match them with incoming gamma spectra from distorted sources. It restores the testing sources’ measurements by finding the closest matching signature patterns in the spectral library. Before HNN recognition, the measured spectrum undergoes preprocessing with a digital image filter to reduce fluctuations. Performance of the proposed method is evaluated using a set of gamma-ray spectra measured with a sodium iodide detector. The data collected include measurements from six pure samples: 241 Am, 60 Co, 137 Cs, 192 Ir, 239 Pu, and 235 U, which are used for training and validation (i.e. six cases). Additionally, the data set contains 24 distorted synthesized sources with various fluctuating backgrounds. Test results demonstrate the potential of the proposed method to accurately recognize the correct isotope with high precision, achieving an accuracy rate exceeding 85%. Furthermore, the proposed method exhibits superior performance compared to the conventional multiple regression fitting and simple feedforward neural network methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2018

Reverse image search for scientific data within and beyond the visible spectrum

The explosion in the rate, quality and diversity of image acquisition instruments has propelled the development of expert systems to organize and query image collections more efficiently. Recommendation systems that handle scientific images are rare, particularly if records lack metadata. This paper introduces new strategies to enable fast searches and image ranking from large pictorial datasets with or without labels. The main contribution is the development of pyCBIR, a deep neural network software to search scientific images by content. This tool exploits convolutional layers with locality sensitivity hashing for querying images across domains through a user-friendly interface. Our results report image searches over databases ranging from thousands to millions of samples. We test pyCBIR search capabilities using three convNets against four scientific datasets, including samples from cell microscopy, microtomography, atomic diffraction patterns, and materials photographs to demonstrate 95% accurate recommendations in most cases. Furthermore, all scientific data collections are released.

97 MATHEMATICS AND COMPUTING↗