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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1983

A two-component X-ray spectrum from SMC X-1

HEAO 1 A-2 and Einstein Solid State Spectrometer (SSS) observations of SMC X-1 are presented. An unpulsed soft component is found with a blackbody temperature of 0.16 keV and an area for the emission region of 10 to the 15th to 10 to the 17th sq cm. The hard X-ray component is pulsed; the phase-averaged spectrum is a power law with alpha of about 0.5 up to 17 keV, above which it steepens. The SSS sets an upper limit of less than 4 x 10 to the 21st H/sq cm to any absorption, and is consistent with that expected from the wind of Sk 160. Absorption dips with a timescale of several hundred seconds are seen immediately following an eclipse exit, and are probably caused by inhomogeneities in the wind of Sk 160.

Marshall, F. E.↗
NASA NTRS1985

The young SMC cluster NGC 330

A color-magnitude diagram has been obtained for the young SMC cluster NGC 330. The diagram shows a well-defined main sequence, a group of blue supergiants, a group of red supergiants between B-V = 1.2 m and 1.6 m about one magnitude fainter, and an empty Hertzsprung gap. The surrounding field is a composite of a very gold population resembling galactic globular clusters and a very young population. DDO and infrared photometry strongly suggest that the cluster is metal-poor, but a definitive measure could not be made because of calibration difficulties. The cluster's age is estimated at 12 million years, with the surrounding field about 50 percent older. The cluster will prove very useful in testing stellar evolution models for young, metal-poor stars if the cluster's metallicity can be established via high-resolution spectroscopy.

Carney, B. W.↗
NASA NTRS1991

Spectral types of stars in the SMC associations NGC456, NGC460a, b and NGC465

Spectral types of stars for an extended area (1200 sq arcmin) of the SMC, containing the associations NGC456, NGC460 a, b and NGC465 have been derived here. High-quality UJ and YJ film copies of objective prism plates taken with the 1.2-m UK Schmidt telescope were used. The medium dispersion spectral (830 A/mm) were digitized with a PDS microdensitometer and spectra types of stars were obtained down to about 16.5 mag. A computer interactive method of reduction has been developed, and the spectral types obtained are accurate to 0.3 spectral subtypes. Spectral classification for the low (2440 A/mm) dispersion spectra down to 18.5 mag was carried out visually using a binocular microscope. The frequency distributions of stars in the associations region, after subtracting the field stars, have revealed that only blue main sequence stars are their members.

Dapergolas, A.↗
NASA NTRS1993

Discovery of orbital decay in SMC X-1

Three observations of the binary X-ray pulsar SMC X-1 with the Ginga satellite, conducted on 3 days in 1987 May, 8.4 days in 1988 August-September, and 4 days in 1989 July-August, are reported. Based on the three observation epochs, the rate of change in the orbital period is estimated at (-3.36 +/- 0.02) x 10 exp -6/yr. An interpretation of the orbital decay is made in the context of tidal evolution with allowance for the effect of the increasing moment of inertia of the companion star due to its nuclear evolution. While the orbital decay is thought to be driven by tidal interactions, it is suggested that the asynchronism between the orbit and the rotation of the companion star is most likely maintained by the evolutionary expansion of the companion star (Sk 160) rather than via the Darwin instability.

Levine, A.↗
NASA NTRS1994

Determination of the atmospheric structure of the BO star companion of SMC X-1 by analysis of Ginga observations

The x-ray phenomena of the binary system SMC X-1/Sk 160, observed with the Ginga and ROSAT x-ray observatories, are compared with computed phenomena derived from a three dimensional hydrodynamical model of the stellar wind perturbed by x-ray heating and ionization which is described in the accompanying paper. In the model the BOI primary star has a line-driven stellar wind in the region of the x-ray shadow and a thermal wind in the region heated by x-rays. We find general agreement between the observed and predicted x-ray spectra throughout the binary orbit cycle, including the extended, variable, and asymmetric eclipse transitions and the period of deep eclipse.

Clark, George W.↗
NASA NTRS1995

Wind dynamics in SMC X-1. 2: Ginga and ROSAT observations

The X-ray phenomena of the binary system SMC X-1/Sk 160, observed with the Ginga and ROSAT X-ray observatories, are compared with computed phenomena derived from a three-dimensional hydrodynamical model of the stellar wind perturbed by X-ray heating and ionization which is described in the accompanying paper (Blondin & Woo 1995). In the model the B0 I primary star has a line-driven stellar wind in the region of the X-ray shadow and a thermal wind in the region heated by X-rays. We find general agreement between the observed and predicted X-ray spectrum throughout the binary orbit cycle, including the extended, variable, and asymmetric eclipse transitions and the period of deep eclipse.

Woo, Jonathan W.↗
NASA NTRS2002

Role of Rotation in Massive Stars in the SMC

We report on an analysis of FUSE+STIS+optical spectra of 17 O-type stars in the SMC. We found an enormous range in N abundances. Three stars in the sample have the same (low) CN abundances as the nebular material out of which they formed, namely C=0.08 C(sub circle dot) and N=0.03 N(sub circle dot). However, more than half shows N~O, an enrichment factor of 30X! Such a high level of N enrichment cannot be reproduced by current evolutionary models accounting for rotationally induced mixing. It suggests that the sum of CNO nuclei may not be conserved, i.e. massive stars might be producing primary nitrogen. It raises questions concerning the sources of nitrogen in the early universe, presently thought to be almost exclusively intermediate-mass stars. It also raises basic questions about the evolution of massive stars in low-metallicity environments, including the precursors to supernovae.

Heap, Sara R.↗
NASA NTRS2004

An X-Ray Investigation of the NGC346 Field in the SMC (3): XMM-Newton Data

We present new XMM-Newton results on the field around the NGC346 star cluster in the SMC. This continues and extends previously published work on Chandra observations of the same field. The two XMM-Newton observations were obtained, respectively, six months before and six months after the previously published Chandra data. Of the 51 X-ray sources detected with XMM-Newton, 29 were already detected with Chandru. Comparing the properties of these X-ray sources in each of our three datasets has enabled us to investigate their variability on times scales of a year. Changes in the flux levels and/or spectral properties were observed for 21 of these sources. In addition, we discovered long-term variations in the X-ray properties of the peculiar system HD5980, a luminous blue variable star, that is likely to be a colliding wind binary system, which displays the largest luminosity during the first XMM-Newton observation.

Naze, Yael↗
NASA NTRS2016

Science and Technology (S and T) Roadmap Collaboration between SMC, NASA, and Government Partners

National Security Space (NSS) presents multi-faceted S and T challenges. We must continually innovate enterprise and information management; provide decision support; develop advanced materials; enhance sensor technology; transform communication technology; develop advanced propulsion and resilient space architectures and capabilities; and enhance multiple additional S and T domains. These challenges are best met by leveraging advanced S and T research and technology development from a number of DoD agencies and civil agencies such as NASA. The authors of this paper have engaged in these activities since 2006 and over the past decade developed multiple strategic S and T relationships. This paper highlights the Office of the Space Missile Systems Center (SMC) Chief Scientist (SMC/ST) collaboration with the NASA Office of Chief Technologist (NASA OCT), which has multiple S and T activities that are relevant to NSS. In particular we discuss the development of the Technology Roadmaps that benefit both Civil Space and NSS. Our collaboration with NASA OCT has been of mutual benefit to multiple participants. Some of the other DoD components include the Defense Advanced Research Projects agency (DARPA), Air Force Research Laboratory (AFRL), Naval Research Laboratory (NRL), The USAF Office of Chief Scientist, the USAF Science Advisory Board (SAB), Space and Naval Warfare Systems Command (SPAWAR), and a number of other services and agencies. In addition, the human talent is a key enabler of advanced S and T activities; it is absolutely critical to have a strong supply of talent in the fields of Science Technology, Engineering, and Mathematics (STEM). Consequently, we continually collaborate with the USAF Institute of Technology (AFIT), other service academies and graduate schools, and other universities and colleges. This paper highlights the benefits that result from such strategic S and T partnerships and recommends a way forward that will continually build upon these achievements into the future.

Science Technology Collaboration↗
NASA NTRS2020

Modeling the Precession of the Warped Inner Accretion Disk in the Pulsars LMC X-4 and SMC X-1 with NuSTAR and XMM-Newton

We present a broad-band X-ray study of the effect of superorbital periods on X-ray spectra and pulse profiles in the neutron star X-ray binaries LMC X-4 and SMC X-1. These two sources display periodic or quasi-periodic variations in luminosity on the order of tens of days which are known to be superorbital, and are attributed to warped, precessing accretion disks. Using joint NuSTAR and XMM-Newton observations that span a complete superorbital cycle, we examine the broad-band spectra of these sources and find the shape to be well described by an absorbed power law with a soft blackbody component. Changes in spectral shape and pulse profile shape are periodic with superorbital period, as expected from a precessing disk. We perform X-ray tomography using the changes in pulse profiles to model the geometry and kinematics of the inner accretion disk. Our simple beam and inner disk geometric model indicates that the long term changes in soft pulse shape and phase are consistent with reprocessed emission from a precessing inner disk.

Brumback, McKinley C.↗
NASA NTRS2022

Sequential Star Formation in the Young SMC Region NGC 602: Insights from ALMA

NGC 602 is a young, low-metallicity star cluster in the "Wing" of the Small Magellanic Cloud. We reveal the recent evolutionary past of the cluster through analysis of high-resolution (∼0.4 pc) Atacama Large Millimeter/submillimeter Array observations of molecular gas in the associated H ii region N90. We identify 110 molecular clumps (R < 0.8 pc) traced by CO emission, and study the relationship between the clumps and associated young stellar objects (YSOs) and pre-main-sequence (PMS) stars. The clumps have high virial parameters (typical αvir = 4–11) and may retain signatures of a collision in the last ≲8 Myr between H i components of the adjacent supergiant shell SMC-SGS 1. We obtain a CO-bright-to-H2 gas conversion factor of XCO,B = (3.4 ± 0.2) × 10 20 cm−2 (K km s −1 ) −1 , and correct observed clump properties for CO-dark H 2 gas to derive a total molecular gas mass in N90 of 16,600 ± 2400 M ⊙ . We derive a recent (≲1 Myr) star formation rate of 130 ± 30 M ⊙ Myr −1 with an efficiency of 8% ± 3% assessed through comparing total YSO mass to total molecular gas mass. Very few significant radial trends exist between clump properties or PMS star ages and distance from NGC 602. We do not find evidence for a triggered star formation scenario among the youngest (≲2 Myr) stellar generations, and instead conclude that a sequential star formation process in which NGC 602 did not directly cause recent star formation in the region is likely.

Theo J O’Neill↗
DOE OSTI2015

Star formation rates from young-star counts and the structure of the ISM across the NGC 346/N66 complex in the SMC

The rate at which interstellar gas is converted into stars, and its dependence on environment, is one of the pillars on which our understanding of the visible Universe is build. We present a comparison of the surface density of young stars (Σ * ) and dust surface density (Σdust) across NGC 346 (N66) in 115 independent pixels of 6 × 6 pc 2 . We find a correlation between Σ * and Σdust with a considerable scatter. A power-law fit to the data yields a steep relation with an exponent of 2.6 ± 0.2. We convert Σdust to gas surface density (Σgas) and Σ * to star formation rate (SFR) surface densities (ΣSFR), using simple assumptions for the gas-to-dust mass ratio and the duration of star formation. The derived total SFR (4 ± 1×10 –3 M ⊙ yr –1 ) is consistent with SFR estimated from the Hα emission integrated over the Hα nebula. On small scales the ΣSFR derived using Hα systematically underestimates the count-based ΣSFR, by up to a factor of 10. This is due to ionizing photons escaping the area, where the stars are counted. We find that individual 36 pc 2 pixels fall systematically above integrated disc galaxies in the Schmidt–Kennicutt diagram by on average a factor of ~7. The NGC 346 average SFR over a larger area (90 pc radius) lies closer to the relation but remains high by a factor of ~3. The fraction of the total mass (gas plus young stars) locked in young stars is systematically high (~10 per cent) within the central 15 pc and systematically lower outside (2 per cent), which we interpret as variations in star formation efficiency. The inner 15 pc is dominated by young stars belonging to a centrally condensed cluster, while the outer parts are dominated by a dispersed population. Furthermore, the observed trend could reflect a change of star formation efficiency between clustered and non-clustered star formation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

SU-E-T-240: Monte Carlo Modelling of SMC Proton Nozzles Using TOPAS

Purpose: To simulate the 60Co beam from a novel Gamma—Tomo SBRT system and to calculate the output factors and dose rates for different source configurations and collimator sizes. Methods: The BEAM-Monte Carlo code is used to realistically model the system geometry, including 32 source capsules, source housing, primary collimator and 4 different changeable collimators. The sources (6.22 mm of diameter) are located in 2 rows with different angles and distances to the longitudinal axis and the beams can be collimated to obtain four different circular field sizes at the isocenter (35 mm, 16 mm, 7mm, and 3.5mm). Since the dose distributions will be determined in a spherical polystyrene phantom of 8cm radius centered at the isocenter, a new geometry module is designed for the dose calculation. Results: : The caracteristics of the particle spectra emerging from each source are determined and the influence of the source position on the spectra reaching the isocenter is found to be minimal (<2%). We found that the scattered photons represent about 15% of the total number of photons reaching the scoring plane immediately (1 mm) below the source and primary collimator. The radial photon fluence does not vary significantly inside the collimator opening and decreases quickly beyond the 3cm radius (projected at the isocenter plane). Conclusion: Our preliminary results indicate that the position of the source and primary collimator assembly does not affect the energy spectra and fluence distributions reaching the isocenter significantly. This allows the use of a well-represented source model for all the sources to perform the dose calculations for all the changeable collimators for this novel Gamma-Tomo SBRT system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

SMC 2021 : Analyzing Resource Utilization and User Behavior on Titan Supercomputer

Resource utilization statistics of submitted jobs on a supercomputer can help us understand how users from various scientific domains use HPC platforms and better design a job scheduler. We explore to generate insight regarding workload distribution and usage pattern domains from job scheduler trace, GPU failure information, and project-specific information collected from Titan supercomputer. Furthermore, we want to know how the scheduler performance varies over time and how the users' scheduling behavior changes following a system failure. These observations have the potential to provide valuable insight, which is helpful to prepare for system failures. These practices will help us develop and apply novel machine learning algorithms in understanding system behavior, requirement, and better scheduling of HPC systems. There are two datasets, RUR and GPU. RUR: This dataset is the job scheduler traces collected from the Titan supercomputerfrom 01/01/2015 to 07/31/2019 (2015.csv - 2019.csv). These were collected usingResource Utilization Report (RUR), a Cray-developed resource-usage data collectionand reporting system. It contains the usage information of its critical resources (CPU,Memory, GPU, and I/O) of each running job on Titan during that period [2]. ProjectAreas: Every job is associated with a project ID. TheProjectAreas.csvdatasetprovides a mapping of the project ID to its domain science. GPU: There have been some hardware-related issues in the GPUs in Titan that caused some GPUs to fail, sometimes irrecoverably during some job runs. This dataset provides information regarding these failures during the execution of the submitted jobs. GPUs on Titan are uniquely identified by a serial number (SN), and they are installed in a location. A GPU can be installed in a location, then removed from that location following a failure, and then re-installed in a different location after fixing the problem. If the failure can't be recovered, the GPU might be removed entirely from Titan. There are two prominent types of failures that resulted in the removal of GPUs from Titan: Double Bit Error (DBE) and Out of the Bus (OTB). The dataset (gc_full.csv) has the following fields: 1. SN : Serial number of a GPU 2. location : The location where it is installed 3. insert : The time when it was inserted into that location 4. remove : The time when it was removed from that location 5. duration : Amount of time the GPU spent in this location 6. out : If the device was taken out entirely w/o a re-installment into a new location. 7. event : If the GPU was taken out entirely, the reason for its removal. To learn more about this dataset, please refer to the git repositoryhttps://github.com/olcf/TitanGPULifeand the related publication [1]. References [1] George Ostrouchov, Don Maxwell, Rizwan A Ashraf, Christian Engelmann, MallikarjunShankar, and James H Rogers. Gpu lifetimes on titan supercomputer: Survival analysisand reliability. InSC20: International Conference for High Performance Computing,Networking, Storage and Analysis, pages 1-14. IEEE, 2020. [2] Feiyi Wang, Sarp Oral, Satyabrata Sen, and Neena Imam. Learning from five-yearresource-utilization data of titan system. In2019 IEEE International Conference onCluster Computing (CLUSTER), pages 1-6. IEEE, 2019.

42 ENGINEERING↗
DOE OSTI2021

SMC 2021 Data Challenge: Analyzing Resource Utilization and User Behavior on Titan Supercomputer

Resource utilization statistics of submitted jobs on a supercomputer can help us understand how users from various scientific domains use HPC platforms and better design a job scheduler. We explore to generate insight regarding workload distribution and usage pattern domains from job scheduler trace, GPU failure information, and project-specific information collected from Titan supercomputer. Furthermore, we want to know how the scheduler performance varies over time and how the users' scheduling behavior changes following a system failure. These observations have the potential to provide valuable insight, which is helpful to prepare for system failures. These practices will help us develop and apply novel machine learning algorithms in understanding system behavior, requirement, and better scheduling of HPC systems. There are two datasets, RUR and GPU: RUR dataset is the job scheduler traces collected from the Titan supercomputer from 01/01/2015 to 07/31/2019 (2015.csv - 2019.csv). These were collected using resource Utilization Report (RUR), a Cray-developed resource-usage data collection and reporting system. It contains the usage information of its critical resources (CPU, Memory, GPU, and I/O) of each running job on Titan during that period (https://ieeexplore.ieee.org/abstract/document/8891001). It includes ProjectAreas as additional information, every job is associated with a project ID. TheProjectAreas.csv dataset provides a mapping of the project ID to its domain science. GPU dataset has information regarding GPU failure on Titan. There have been some hardware-related issues in the GPUs in Titan that caused some GPUs to fail, sometimes irrecoverably during some job runs. This dataset provides information regarding these failures during the execution of the submitted jobs. GPUs on Titan are uniquely identified by a serial number (SN), and they are installed in a location. A GPU can be installed in a location, then removed from that location following a failure, and then re-installed in a different location after fixing the problem. If the failure can't be recovered, the GPU might be removed entirely from Titan. There are two prominent types of failures that resulted in the removal of GPUs from Titan: Double Bit Error (DBE) and Out of the Bus (OTB). The dataset (gc_full.csv) has seven attributes, we provided a short description of these attributes in the ReadMe file. To learn more about this dataset, please refer to the git repository https://github.com/olcf/TitanGPULife and the related publication (https://ieeexplore.ieee.org/abstract/document/9355319).

42 ENGINEERING↗
DOE OSTI2020

VULCAN temperature dataset for SMC data challenge

The VULCAN Beamline dataset provides the sample measurement, where temperatures is recorded in two physically different places on the sample. These are held in two different hdf5 groups in the data file.

42 ENGINEERING↗