Thermal stress response of General Purpose Heat Source (GPHS) aeroshell material

A thermal stress test was conducted to determine the ability of the GPHS aeroshell 3 D FWPF material to maintain physical integrity when exposed to a severe heat flux such as would occur from prompt reentry of GPHS modules. The test was performed in the Giant Planetary Facility at NASA's Ames Research Center. Good agreement was obtained between the theoretical and experimental results for both temperature and strain time histories. No physical damage was observed in the test specimen. These results provide initial corroboration both of the analysis techniques and that the GPHS reentry member will survive the reentry thermal stress levels expected

Photovoltage in curved 1D systems

Curvature of quantum wire results in intrasubband absorption of IR radiation that induces stationary photovoltage in presence of circular polarization. This effect is studied in ballistic (collisionless) and kinetic regimes. The consideration is concentrated on quantum wires with curved central part. It is shown, that if mean free path is shorter than length of the curved part the photovoltage does not depend on the wire shape, but on the total angle of rotation of wire tangent. It is not the case when mean free path is finite or large. This situation was studied for three specific shapes of wires: "hard angle", "open book" and "$\Omega$-like".Comment: 12 pages, 1 figur

Degradation and reuse of radiative thermal protection system materials for the space shuttle

Three silicide coated columbium alloys and two cobalt alloys were subjected to identical simulated reentry profiling exposures in both static (controlled vacuum leak) and dynamic (hypersonic plasma shear) environments. Primary emphasis in the columbium alloy evaluation was on the Cb752 and C129Y alloys with a lesser amount on FS85. Commercial silicide coatings of the R512E and VH109 formulations were used. The coated specimens were intentionally defected to provide the types of coating flaws that are expected in service. Temperatures were profiled up to peak temperatures of either 2350 F or 2500 F for 15 minutes in each cycle

Stationary drag photocurrent caused by strong running wave in quantum wire: quantization of current

The stationary current induced by a strong running potential wave in one-dimensional system is studied. Such a wave can result from illumination of a straight quantum wire with special grating or spiral quantum wire by circular-polarized light. The wave drags electrons in the direction correlating with the direction of the system symmetry and polarization of light. In a pure system the wave induces minibands in the accompanied system of reference. We study the effect in the presence of impurity scattering. The current is an interplay between the wave drag and impurity braking. It was found that the drag current is quantized when the Fermi level gets into energy gaps

Variability in high-mass X-ray binaries

Strongly magnetized, accreting neutron stars show periodic and aperiodic variability over a wide range of time scales. By obtaining spectral and timing information on these different time scales, we can have a closer look into the physics of accretion close to the neutron star and the properties of the accreted material. One of the most prominent time scales is the strong pulsation, i.e., the rotation period of the neutron star itself. Over one rotation, our view of the accretion column and the X-ray producing region changes significantly. This allows us to sample different physical conditions within the column but at the same time requires that we have viewing-angle-resolved models to properly describe them. In wind-fed high-mass X-ray binaries, the main source of aperiodic variability is the clumpy stellar wind, which leads to changes in the accretion rate (i.e., luminosity) as well as absorption column. This variability allows us to study the behavior of the accretion column as a function of luminosity, as well as to investigate the structure and physical properties of the wind, which we can compare to winds in isolated stars.Comment: 6 pages, 4 figures, accepted for publication in Astronomische Nachrichten (proceedings of the XMM-Newton Workshop 2019

Simultaneous multiwavelength observations of V404 Cygni during its 2015 June outburst decay strengthen the case for an extremely energetic jet-base

We present results of multiband optical photometry of the black hole X-ray binary system V404 Cygni obtained using Wheaton College Observatory's 0.3m telescope, along with strictly simultaneous INTEGRAL and Swift observations during 2015 June 25.15--26.33 UT, and 2015 June 27.10--27.34 UT. These observations were made during the 2015 June outburst of the source when it was going through an epoch of violent activity in all wavelengths ranging from radio to $\gamma$-rays. The multiwavelength variability timescale favors a compact emission region, most likely originating in a jet outflow, for both observing epochs presented in this work. The simultaneous INTEGRAL/Imager on Board the Integral Satellite (IBIS) 20--40 keV light curve obtained during the June 27 observing run correlates very strongly with the optical light curve, with no detectable delay between the optical bands as well as between the optical and hard X-rays. The average slope of the dereddened spectral energy distribution was roughly flat between the $I_C$- and $V$-bands during the June 27 run, even though the optical and X-ray flux varied by $>$25$\times$ during the run, ruling out an irradiation origin for the optical and suggesting that the optically thick to optically thin jet synchrotron break during the observations was at a frequency larger than that of $V$-band, which is quite extreme for X-ray binaries. These observations suggest that the optical emission originated very close to the base of the jet. A strong H$\alpha$ emission line, probably originating in a quasi-spherical nebula around the source, also contributes significantly in the $R_C$-band. Our data, in conjunction with contemporaneous data at other wavelengths presented by other groups, strongly suggest that the jet-base was extremely compact and energetic during this phase of the outburst.Comment: 15 pages, 2 tables, 5 figures. Accepted for publication in Ap

An evaluation of machine-learning for predicting phenotype: studies in yeast, rice, and wheat

Abstract: In phenotype prediction the physical characteristics of an organism are predicted from knowledge of its genotype and environment. Such studies, often called genome-wide association studies, are of the highest societal importance, as they are of central importance to medicine, crop-breeding, etc. We investigated three phenotype prediction problems: one simple and clean (yeast), and the other two complex and real-world (rice and wheat). We compared standard machine learning methods; elastic net, ridge regression, lasso regression, random forest, gradient boosting machines (GBM), and support vector machines (SVM), with two state-of-the-art classical statistical genetics methods; genomic BLUP and a two-step sequential method based on linear regression. Additionally, using the clean yeast data, we investigated how performance varied with the complexity of the biological mechanism, the amount of observational noise, the number of examples, the amount of missing data, and the use of different data representations. We found that for almost all the phenotypes considered, standard machine learning methods outperformed the methods from classical statistical genetics. On the yeast problem, the most successful method was GBM, followed by lasso regression, and the two statistical genetics methods; with greater mechanistic complexity GBM was best, while in simpler cases lasso was superior. In the wheat and rice studies the best two methods were SVM and BLUP. The most robust method in the presence of noise, missing data, etc. was random forests. The classical statistical genetics method of genomic BLUP was found to perform well on problems where there was population structure. This suggests that standard machine learning methods need to be refined to include population structure information when this is present. We conclude that the application of machine learning methods to phenotype prediction problems holds great promise, but that determining which methods is likely to perform well on any given problem is elusive and non-trivial