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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A classification of invertible phases of bosonic quantum lattice systems in one dimension

We study invertible states of 1D bosonic quantum lattice systems. Here we show that every invertible 1D state is in a trivial phase: after tensoring with some unentangled ancillas, it can be disentangled by a fuzzy analog of a finite-depth quantum circuit. If an invertible state has symmetries, it may be impossible to disentangle it in a way that preserves the symmetries, even after adding unentagled ancillas. We show that in the case of a finite unitary symmetry G , the only obstruction is an index valued in degree-2 cohomology of G . We show that two invertible G -invariant states are in the same phase if and only if their indices coincide.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Classification of computed thermal tomography images with deep learning convolutional neural network

Thermal tomography (TT) is a computational method for the reconstruction of depth profile of the internal material defects from Pulsed Infrared Thermography (PIT) nondestructive evaluation. Here, the PIT method consists of recording material surface temperature transients with a fast frame infrared camera, following thermal pulse deposition on the material surface with a flashlamp and heat diffusion into material bulk. TT algorithm obtains depth reconstructions of thermal effusivity, which has been shown to provide visualization of the subsurface internal defects in metals. In many applications, one needs to determine the defect shape and orientation from reconstructed effusivity images. Interpretation of TT images is non-trivial because of blurring, which increases with depth due to the heat diffusion-based nature of image formation. We have developed a deep learning convolutional neural network (CNN) to classify the size and orientation of subsurface material defects in TT images. CNN was trained with TT images produced with computer simulations of 2D metallic structures (thin plates) containing elliptical subsurface voids. The performance of CNN was investigated using test TT images developed with computer simulations of plates containing elliptical defects, and defects with shapes imported from scanning electron microscopy images. CNN demonstrated the ability to classify radii and angular orientation of elliptical defects in previously unseen test TT images. We have also demonstrated that CNN trained on the TT images of elliptical defects is capable of classifying the shape and orientation of irregular defects.

42 ENGINEERING↗
DOE OSTI2023

Demonstration of object location, classification, and characterization by developed deep learning dust ablation trail analysis code package using plasma jets

Based on deep learning, a Dust Ablation Trail Analysis (DATA) code package is developed to detect dust ablation trails in tokamaks, which is intended to analyze a large amount data of tokamak dusts. To validate and benchmark the DATA code package, 2440 plasma jet images are exploited for the training and test of the deep learning DATA code package, since plasma jets resemble the shape and size of dust ablation clouds in tokamaks. After being trained by 1920 plasma jet images, the DATA code package is able to locate 100% plasma jets, classify plasma jets with the accuracy of >99.9%, and output image skeleton information for classified plasma jets. The DATA code package trained by the plasma jet images is also used to analyze the dust ablation trails captured in the Experimental Advanced Superconducting (EAST) tokamak with the satisfactory performance, further verifying its applicability in the fusion dust ablation investigation. Based on its excellent performance presented here, it is demonstrated that our DATA code package is able to automatically identify and analyze dust ablation trails in tokamaks, which can be used for further detailed investigations, such as the three-dimensional reconstruction of dusts and their ablation trails.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Universal and interpretable classification of atomistic structural transitions via unsupervised graph learning

Materials processing often occurs under extreme dynamic conditions leading to a multitude of unique structural environments. These structural environments generally occur at high temperatures and/or high pressures, often under non-equilibrium conditions, which results in drastic changes in the material's structure over time. Computational techniques, such as molecular dynamics simulations, can probe the atomic regime under these extreme conditions. However, characterizing the resulting diverse atomistic structures as a material undergoes extreme changes in its structure has proved challenging due to the inherently non-linear relationship between structures as large-scale changes occur. Here, we introduce SODAS++, a universal graph neural network framework, that can accurately and intuitively quantify the atomistic structural evolution corresponding to the transition between any two arbitrary phases. We showcase SODAS++ for both solid–solid and solid–liquid transitions for systems of increasing geometric and chemical complexity, such as colloidal systems, elemental Al, rutile and amorphous TiO 2 , and the non-stoichiometric ternary alloy Ag 26 Au 5 Cu 19 . Finally, we show that SODAS++ can accurately quantify all transitions in a physically interpretable manner, showcasing the power of unsupervised graph neural network encodings for capturing the complex and non-linear pathway, a material's structure takes as it evolves.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Classification of nondegenerate equilibria and degenerate 1-dimensional orbits of the Kovalevskaya-Yehia integrable system

The paper is devoted to a topological analysis of the Kovalevskaya-Yehia integrable case in rigid body dynamics. It is proved that the integral has the Bott property on isoenergy surfaces of the system; the topology of the Liouville foliation in a neighbourhood of degenerate 1-dimensional orbits and equilibria (points of rank 0) is also described. In particular, marked loop molecules are constructed for degenerate 1-dimensional orbits, and a representation in the form of an almost direct product is found for nondegenerate singularities of rank 0. Bibliography: 17 titles.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Classification of billiard motions in domains bounded by confocal parabolas

We consider the billiard dynamical system in a domain bounded by confocal parabolas. We describe such domains in which the billiard problem can be correctly stated. In each such domain we prove the integrability for the system, analyse the arising Liouville foliation, and calculate the invariant of Liouville equivalence--the so-called marked molecule. It turns out that billiard systems in certain parabolic domains have the same closures of solutions (integral trajectories) as the systems of Goryachev-Chaplygin-Sretenskii and Joukowski at suitable energy levels. We also describe the billiard motion in noncompact domains bounded by confocal parabolas, namely, we describe the topology of the Liouville foliation in terms of rough molecules. Bibliography: 16 titles.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

e/a classification of Hume–Rothery Rhombic Triacontahedron-type approximants based on all-electron density functional theory calculations

There are three key electronic parameters in elucidating the physics behind the Hume–Rothery electron concentration rule: the square of the Fermi diameter (2kF)2, the square of the critical reciprocal lattice vector and the electron concentration parameter or the number of itinerant electrons per atom e/a. We have reliably determined these three parameters for 10 Rhombic Triacontahedron-type 2/1–2/1–2/1 (N = 680) and 1/1–1/1–1/1 (N = 160–162) approximants by making full use of the full-potential linearized augmented plane wave-Fourier band calculations based on all-electron density-functional theory. We revealed that the 2/1–2/1–2/1 approximants Al13Mg27Zn45 and Na27Au27Ga31 belong to two different sub-groups classified in terms of equal to 126 and 109 and could explain why they take different e/a values of 2.13 and 1.76, respectively. Among eight 1/1–1/1–1/1 approximants Al3Mg4Zn3, Al9Mg8Ag3, Al21Li13Cu6, Ga21Li13Cu6, Na26Au24Ga30, Na26Au37Ge18, Na26Au37Sn18 and Na26Cd40Pb6, the first two, the second two and the last four compounds were classified into three sub-groups with = 50, 46 and 42; and were claimed to obey the e/a = 2.30, 2.10–2.15 and 1.70–1.80 rules, respectively.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Supervised classification of electric power transmission line nominal voltage from high-resolution aerial imagery

For many researchers, government agencies, and emergency responders, access to the geospatial data of US electric power infrastructure is invaluable for analysis, planning, and disaster recovery. Historically, however, access to high quality geospatial energy data has been limited to few agencies because of commercial licenses restrictions, and those resources which are widely accessible have been of poor quality, particularly with respect to reliability. Recent efforts to develop a highly reliable and publicly accessible alternative to the existing datasets were met with numerous challenges – not the least of which was filling the gaps in power transmission line voltage ratings. To address the line voltage rating problem, we developed and tested a basic methodology that fuses knowledge and techniques from power systems, geography, and machine learning domains. Specifically, we identified predictors of nominal voltage that could be extracted from aerial imagery and developed a tree-based classifier to classify nominal line voltage ratings. Overall, we found that line support height, support span, and conductor spacing are the best predictors of voltage ratings, and that the classifier built with these predictors had a reliable predictive accuracy (that is, within one voltage class for four out of the five classes sampled). In conclusion, we applied our approach to a study area in Minnesota.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2012

CHANDRA OBSERVATIONS AND CLASSIFICATION OF ACTIVE GALACTIC NUCLEUS CANDIDATES CORRELATED WITH AUGER UHECRs

We report on Chandra X-ray observations of possible active galactic nuclei (AGNs) which have been correlated with ultrahigh energy cosmic rays (UHECRs) observed by the Pierre Auger Collaboration. Combining our X-ray observations with optical observations, we conclude that one-third of the 21 Veron-Cetty Veron (VCV) galaxies correlating with UHECRs in the first Auger data release are actually not AGNs. We review existing optical observations of the 20 VCV galaxies correlating with UHECRs in the second Auger data release and determine that three of them are not AGNs and two are uncertain. Overall, of the 57 published UHECRs with |b| > 10 Degree-Sign , 22 or 23 correlate with true AGNs using the Auger correlation parameters. We also measured the X-ray luminosity of ESO139-G12 to complete the determination of the bolometric luminosities of AGNs correlating with UHECRs in the first data set. Apart from two candidate sources which require further observation, we determined bolometric luminosities for the candidate galaxies of the second data set. We find that only 2 of the total of 69 published UHECRs correlate with AGNs (IC5135 and IC4329a) which are powerful enough in their steady state to accelerate protons to the observed energies of their correlated UHECRs. The GZK expectation is that {approx}45% of the sources of UHECRs above 60 EeV should be contained within the z < 0.018 volume defined by the Auger scan analysis, so an observed level of 30%-50% correlation with weak AGNs is compatible with the suggestion that AGNs experience transient high-luminosity states during which they accelerate UHECRs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

A COMPREHENSIVE ANALYSIS OF FERMI GAMMA-RAY BURST DATA. III. ENERGY-DEPENDENT T {sub 90} DISTRIBUTIONS OF GBM GRBs AND INSTRUMENTAL SELECTION EFFECT ON DURATION CLASSIFICATION

The durations (T {sub 90}) of 315 gamma-ray bursts (GRBs) detected with Fermi/GBM (8-1000 keV) up to 2011 September are calculated using the Bayesian Block method. We compare the T {sub 90} distributions between this sample and those derived from previous/current GRB missions. We show that the T {sub 90} distribution of this GRB sample is bimodal, with a statistical significance level comparable to those derived from the BeppoSAX/GRBM sample and the Swift/BAT sample, but lower than that derived from the CGRO/BATSE sample. The short-to-long GRB number ratio is also much lower than that derived from the BATSE sample, i.e., 1:6.5 versus 1:3. We measure T {sub 90} in several bands, i.e., 8-15, 15-25, 25-50, 50-100, 100-350, and 350-1000 keV, to investigate the energy-dependence effect of the bimodal T {sub 90} distribution. It is found that the bimodal feature is well observed in the 50-100 and 100-350 keV bands, but is only marginally acceptable in the 25-50 keV and 350-1000 keV bands. The hypothesis of bimodality is confidently rejected in the 8-15 and 15-25 keV bands. The T {sub 90} distributions in these bands are roughly consistent with those observed by missions with similar energy bands. The parameter T {sub 90} as a function of energy follows T-bar {sub 90}{proportional_to}E{sup -0.20{+-}0.02} for long GRBs. Considering the erratic X-ray and optical flares, the duration of a burst would be even longer for most GRBs. Our results, together with the observed extended emission of some short GRBs, indicate that the central engine activity timescale would be much longer than T {sub 90} for both long and short GRBs and the observed bimodal T {sub 90} distribution may be due to an instrumental selection effect.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

SHORT VERSUS LONG AND COLLAPSARS VERSUS NON-COLLAPSARS: A QUANTITATIVE CLASSIFICATION OF GAMMA-RAY BURSTS

Gamma-ray bursts (GRBs) are traditionally divided into long and short according to their durations (lg2 s). It was generally believed that this reflects a different physical origin: collapsars (long) and non-collapsars (short). We have recently shown that the duration distribution of collapsars is flat, namely, independent of the duration, at short durations. Using this model for the distribution of Collapsars we determine the duration distribution of non-Collapsars and estimate the probability that a burst with a given duration (and hardness) is a Collapsar or not. We find that this probability depends strongly on the spectral window of the observing detector. While the commonly used limit of 2 s is conservative and suitable for BATSE bursts, 40% of Swift's bursts shorter than 2 s are Collapsars and the division lg0.8 s is more suitable for Swift. We find that the duration overlap of the two populations is very large. On the one hand there is a non-negligible fraction of non-Collapsars longer than 10 s, while on the other hand even bursts shorter than 0.5 s in the Swift sample have a non-negligible probability to be Collapsars. Our results enable the construction of non-Collapsar samples while controlling the Collapsar contamination. They also highlight that no firm conclusions can be drawn based on a single burst and they have numerous implications concerning previous studies of non-Collapsar properties that were based on the current significantly contaminated Swift samples of localized short GRBs. Specifically (1) all known short bursts with z > 1 are most likely Collapsars; (2) the only short burst with a clear jet break is most likely a Collapsar, indicating our lack of knowledge concerning non-Collapsar beaming; and (3) the existence of non-Collapsars with durations up to 10 s imposes new challenges to non-Collapsar models.

79 ASTRONOMY AND ASTROPHYSICS↗