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At least 1,333 records · Page 74

Helium causing disappearance of a/2<111> dislocation loops in binary Fe-Cr ferritic alloys

In this work, single and dual-beam self-ion irradiations were performed at 500°C on ultra-high purity Fe14%Cr alloy to ~0.33 displacements-per-atom (dpa), and 0 or 3030 atomic-parts-per-million (appm) helium/dpa, respectively. Using transmission electron microscopy, we reveal that helium can drastically modify the dislocation loop Burgers vector in Fe-Cr alloys. Helium co-implantation caused complete disappearance of a/2<111> type dislocation loops, and the microstructure consisted of only a<100> loops. Conversely, a/2<111> type loops were predominant without He co-implantation. The total loop density remained largely unaffected. The results strikingly contrast literature asserting that helium stabilizes a/2<111> type loops in bcc Fe alloys, based on low temperature irradiations. Collectively analyzing the results with literature suggest that the small positive interaction between helium and self-interstitial atoms (SIA) in Fe predicted by atomistic simulations maybe insufficient to holistically explain the dislocation loop microstructure development in presence of helium. Helium-SIA positive binding inadvertently implies elevated a/2<111> loop fraction and higher loop densities that the present results contradict. Helium induced high cavity density causing a preferential loss of highly glissile <111> clusters, leaving the matrix saturated with <100> type clusters is proposed as a potential mechanism. Further, the in-situ irradiations combined with Burgers vector analysis strengthened the evidence of Cr-induced dislocation loop mobility reduction that appears to stabilize the a/2<111> type loops and causes higher loop densities in Fe-Cr alloys.

36 MATERIALS SCIENCE↗

The Be/X-Ray Binary A0535+26 During Its Recent 2009/2010 Outbursts

The Be/X-ray binary A0535+26 showed a giant outburst in December 2009 that reached approximately 5.14 Crab in thc 15-50 keV range. Unfortunately, due to Sun constraints it could not be observed by most X-ray satellites. The outburst was preceded by four weaker outbursts associated with the periastron passage of the neutron star. The fourth of them, in August 2009, presented a peculiar double-peaked light curve, with a first peak lasting about 9 days that reached a (15- 50 keV) flux of 440 mCrab. The tl ux then decreased to less than 220 mCrab, and increased again reaching 440 mCrab around the periastron. The outburst was monitored with INTEGRAL, RXTE, and Suzaku TOO observations. One orbital period (approximately 111 days) after the 2009 giant outburst, a new and unexpectedly bright outburst took place (approximately 1.4Crab in the 15-50 keV range). It was monitored with TOO observations with INTEGRAL, RXTE, Suzaku, and Swift. First results of the spectral and timing analysis of these observations are presented. with a specific focus on the cyclotron lines present in thc system and its variation with the mass accretion rate.

Caballero, I.↗

The Soft State of Cygnus X-1 Observed with NuSTAR: A Variable Corona and a Stable Inner Disk

We present a multi-epoch hard X-ray analysis of Cygnus X-1 in its soft state based on four observations with the Nuclear Spectroscopic Telescope Array (NuSTAR). Despite the basic similarity of the observed spectra, there is clear spectral variability between epochs. To investigate this variability, we construct a model incorporating both the standard disk-corona continuum and relativistic reflection from the accretion disk, based on prior work on Cygnus X-1, and apply this model to each epoch independently. We find excellent consistency for the black hole spin and the iron abundance of the accretion disk, which are expected to remain constant on observational timescales. In particular, we confirm that Cygnus X-1 hosts a rapidly rotating black hole, 0.93 < approx. a* < approx. 0.96, in broad agreement with the majority of prior studies of the relativistic disk reflection and constraints on the spin obtained through studies of the thermal accretion disk continuum. Our work also confirms the apparent misalignment between the inner disk and the orbital plane of the binary system reported previously, finding the magnitude of this warp to be approx.10deg-15deg. This level of misalignment does not significantly change (and may even improve) the agreement between our reflection results and the thermal continuum results regarding the black hole spin. The spectral variability observed by NuSTAR is dominated by the primary continuum, implying variability in the temperature of the scattering electron plasma. Finally, we consistently observe absorption from ionized iron at approx. 6.7 keV, which varies in strength as a function of orbital phase in a manner consistent with the absorbing material being an ionized phase of the focused stellar wind from the supergiant companion star.

black hole physics↗

Eta Car: The Good, the Bad and the Ugly of Nebular and Stellar Confusion

Observations in the far-UV provide a unique opportunity to investigate the very massive star Eta Car and its hot binary companion, Eta Car B. Eta Car was observed with FUSE over a large portion of the 5.54 year spectroscopic period before and after the 2003.5 minimum. The observed spectrum is defined by strong stellar wind signatures, primarily from Eta Car A, complicated by the strong absorptions of the ejecta surrounding Eta Car plus interstellar absorption. The Homunculus and Little Homunculus are massive bipolar ejecta historically associable with LBV outbursts in the 1840s and the 1890s and are linked to absorptions at -513 and -146 km/s, respectively. The FUSE spectra are confused by the extended nebulosity and thermal drifting of the FUSE co-pointed instruments. Interpretation is further complicated by two B-stars sufficiently close to h Car to be included most of the time in the large FUSE aperture. Followup observations partially succeeded in obtaining spectra of at least one of these B-stars through the smaller apertures, allowing potential separation of the B-star contributions and h Car. A complete analysis of all available spectra is currently underway. Our ultimate goals are to directly detect the hot secondary star if possible with FUSE and to identify the absorption contributions to the overall spectrum especially of the stellar members and the massive ejecta.

Gull, T.R.↗

A generalized analytical energy balance model for evaluating agglomeration from a binary collision of wet particles

Agglomeration of wet particles, i.e., particles coated with a thin liquid layer, is a common phenomenon in many processes like fluidized bed combustion of low rank fuels. The availability of an agglomeration model that can evaluate the outcome of a binary collision between wet particles differing in solid particle properties, liquid layer thicknesses, and initial collision (impact) speeds is essential for obtaining a comprehensive understanding on the existing processes experiencing wet particle agglomeration or for a successful development of new processes with high chances of wet particle agglomeration. This study presents a generalized agglomeration model on the basis of energy conservation before and after collision when colliding wet particles may differ in solid particle properties, liquid layer thicknesses, and impact speeds. The model was established based on the approximate values of energy losses that may happen during the collision. It incorporates body forces, solid-solid contacting, liquid capillary, and viscous contributions, as well as the liquid bridge volume effect. Predictions of the new model for collision outcomes of identical wet particles were like those from an analytical energy balance model developed recently by the group for identical wet particles. We also validated the new model by experimental data from literature. The results of a collision direction analysis indicated that the direction often has a minimal effect on the collision outcome in many practical scenarios. The results of Monte Carlo uncertainty analyses with the new model revealed that proper estimations of impact speed, under capillary limiting conditions, and thickness of coating layers and asperity heights, under viscous limiting conditions, are critical for the realistic prediction of collision outcomes at impact speeds close to critical impact speed, i.e., the minimum particle speed required for the particles to rebound.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Probing the evolution of fault properties during the seismic cycle with deep learning

We use seismic waves that pass through the hypocentral region of the 2016 M6.5 Norcia earthquake together with Deep Learning (DL) to distinguish between foreshocks, aftershocks and time-to-failure (TTF). Binary and N-class models defined by TTF correctly identify seismograms in test with > 90% accuracy. We use raw seismic records as input to a 7 layer CNN model to perform the classification. Here we show that DL models successfully distinguish seismic waves pre/post mainshock in accord with lab and theoretical expectations of progressive changes in crack density prior to abrupt change at failure and gradual postseismic recovery. Performance is lower for band-pass filtered seismograms (below 10 Hz) suggesting that DL models learn from the evolution of subtle changes in elastic wave attenuation. Tests to verify that our results indeed provide a proxy for fault properties included DL models trained with the wrong mainshock time and those using seismic waves far from the Norcia mainshock; both show degraded performance. Our results demonstrate that DL models have the potential to track the evolution of fault zone properties during the seismic cycle. If this result is generalizable it could improve earthquake early warning and seismic hazard analysis.

58 GEOSCIENCES↗

Image-driven discriminative and generative machine learning algorithms for establishing microstructure–processing relationships

We investigate methods of microstructure representation for the purpose of predicting processing condition from microstructure image data. A binary alloy that is currently under development as a nuclear fuel was studied for the purpose of developing an improved machine learning approach to image recognition, characterization, and building predictive capabilities linking microstructure to processing conditions. Here, we test different microstructure representations and evaluate model performance based on classification accuracy. A classification accuracy of 95.8% was achieved fordistinguishing between micrographs corresponding to ten different thermo-mechanical material processing conditions.We find that our newly developed microstructure representation describes image data well, and the traditional approachof utilizing area fractions of different phases is insufficient for distinguishing between multiple classes using a relativelysmall, imbalanced original data set of 272 images. To explore the applicability of generative methods for supplementing such limited data sets, generative adversarial networks were trained to generate artificial microstructure images. Two different generative networks were trained and tested to assess performance. Challenges and best practices associated with applying machine learning to limited microstructure image data sets is also discussed. Our work has implications for quantitative microstructure analysis, and development of microstructure-processing relationships in limited data sets typical of metallurgical process design studies.

36 MATERIALS SCIENCE↗

Effect of isopropanol cosolvent on the rheology and spinnability of aqueous polyacrylic acid solutions

Abstract We investigate the effect of alcohol fraction (isopropanol, IPA) in a binary water‐alcohol solvent mixture on the shear and extensional rheological properties, as well as the role of viscoelasticity on fiber formation of poly(acrylic acid) (PAA) in electrospinning. Comparison of the scaling of both specific viscosities η sp and extensional relaxation times λ E of PAA in water–IPA mixtures, showed stronger scaling compared to salt‐free aqueous polyelectrolyte solutions, except for the η sp in the unentangled regime displaying a polyelectrolyte‐like scaling η sp ~ c 0.5 for all IPA%. Such deviation suggested IPA induces association/aggregation of PAA. However, the trends between η sp and λ E magnitudes as a function of IPA% differ for concentrations compared in the entangled regime. The η sp as well as their elastic moduli exhibit a maximum, whereas λ E increases monotonically with IPA%, suggesting a complex interplay of various interactions are dictating their structure in water‐IPA mixtures, affecting their shear and extensional response differently. Electrospinning experiments showed increasing IPA% reduces the onset of both beaded and uniform fibers. Analysis using dimensionless numbers indicated the enhancement of their elasticity by IPA, and the consequent stabilizing effect on their jets/filaments against break‐up during electrospinning, plays a role in the improvement of their fiber formation.

36 MATERIALS SCIENCE↗

Maximum likelihood estimation of difference scaling functions for suprathreshold judgments

Maximum likelihood estimation (MLE) has been used to produce perceptual scales from binary judgments of triads and quadruples. This method relies on Thurstone’s theory of a stochastic perceptual process where the perceived difference of two stimuli is the difference in their perceived strengths. It is possible that the perception of a suprathreshold difference is overestimated when adding smaller differences, a phenomenon referred to as diminishing returns. The current approach to construct a perceptual scale using MLE does not account for this phenomenon. We present a way to model the perception of differences using MLE and Thurstone’s theory, adapted to allow the possibility of diminishing returns. This method is validated using Monte Carlo simulated responses to experimental triads and can correctly model diminishing returns, the absence of diminishing returns, and the opposite of diminishing returns both in the cases when a perceptual scale is known and when the true perceived strengths of the stimuli are unknown. Additionally, this method was applied to empirical data sets to determine its feasibility in investigations of perception. Ultimately, it was found that this analysis allows for more accurate modeling of suprathreshold difference judgments, a more complete understanding of the perceptual processes underlying comparisons, and the evaluation of Thurstone’s theory of difference judgments.

97 MATHEMATICS AND COMPUTING↗

A Posteriori Study of a DNS Database Describing Super critical Binary-Species Mixing

Currently, the modeling of supercritical-pressure flows through Large Eddy Simulation (LES) uses models derived for atmospheric-pressure flows. Those atmospheric-pressure flows do not exhibit the particularities of high densitygradient magnitude features observed both in experiments and simulations of supercritical-pressure flows in the case of two species mixing. To assess whether the current LES modeling is appropriate and if found not appropriate to propose higher-fidelity models, a LES a posteriori study has been conducted for a mixing layer that initially contains different species in the lower and upper streams, and where the initial pressure is larger than the critical pressure of either species. An initially-imposed vorticity perturbation promotes roll-up and a double pairing of four initial span-wise vortices into an ultimate vortex that reaches a transitional state. The LES equations consist of the differential conservation equations coupled with a real-gas equation of state, and the equation set uses transport properties depending on the thermodynamic variables. Unlike all LES models to date, the differential equations contain, additional to the subgrid scale (SGS) fluxes, a new SGS term that is a pressure correction in the momentum equation. This additional term results from filtering of Direct Numerical Simulation (DNS) equations, and represents the gradient of the difference between the filtered pressure and the pressure computed from the filtered flow field. A previous a priori analysis, using a DNS database for the same configuration, found this term to be of leading order in the momentum equation, a fact traced to the existence of high-densitygradient magnitude regions that populated the entire flow; in the study, models were proposed for the SGS fluxes as well as this new term. In the present study, the previously proposed constantcoefficient SGS-flux models of the a priori investigation are tested a posteriori in LES, devoid of or including, the SGS pressure correction term. The present pressure-correction model is different from, and more accurate as well as less computationally intensive than that of the a priori study. The constant-coefficient SGS-flux models encompass the Smagorinsky (SMC), in conjunction with the Yoshizawa (YO) model for the trace, the Gradient (GRC) and the Scale Similarity (SSC) models, all exercised with the a priori study constant coefficients calibrated at the transitional state. The LES comparison is performed with the filtered- and-coarsened (FC) DNS, which represents an ideal LES solution. Expectably, an LES model devoid of SGS terms is shown to be considerably inferior to models containing SGS effects. Among models containing SGS effects, those including the pressure-correction term are substantially superior to those devoid of it. The sensitivity of the predictions to the initial conditions and grid size are also investigated. Thus, it has been discovered that, additional to the atmospheric-pressure models currently used, a new model is necessary to simulate supercritical-pressure flows. This model depends on the thermodynamic characteristics of the chemical species involved.

Bellan, Josette↗

Early Information Parameter-Set Analysis for Satellite Close Approaches using Machine Learning

Understanding orbital mechanics is essential in space flight and navigation applications, and leveraging modern force models for flight path projection remains an important aspect in space mission design and operation. However, force models do not capture all the dynamics or perturbations in the space environment and thus are subject to errors in predicting the state vectors. The further out the predicted miss distance between spacecraft is from the time of closest approach (TCA), the larger the propagated errors in the predicted miss distance at TCA is. The dependency on these force models for spacecraft flight state prediction calls for a more reliable method that can quantify, or even reduce, these propagated errors. With recent advances in the field artificial intelligence, specifically in machine and deep learning algorithms, a model that implements these approaches can improve on the modern force model approach. The goal for this work is to provide an early-information decision-making threshold, in order to prioritize risk assessment implementation, given the ongoing increase of space objects. In analyzing the relationship of several parameters from conjunction data messages(CDMs) and solar information, early information becomes viable in miss distance prediction with unsupervised learning techniques, which learn the parameters that are linked together with miss distance and probability of collision (Pc) variables. Another approach implemented for identifying relationships within CDMs is supervised learning, in which a shallow neural network binary classifier learns to distinguish events with Pc values¡108. These parameters detected in the unsupervised process are then applied to a regression neural network, which predicts the miss distance at TCA for a specific event within a given uncertainty bound. For the regression neural network, a Long Short Term Memory (LSTM) neural network is implemented, which yields memory about each time step in an event. Using an LSTM network, the model learns to predict miss distance within 0.2km of the value measured at TCA. Although there is a limited amount of "close miss" data to train a network, the network learns to associate parameters, like large energy dissipation rates with the secondary object, with an elevated Pc

Brianna I. Robertson↗

Binary pseudorandom array test standard optimized for characterization of large field-of-view optical interferometers

Recently, a technique for calibrating the modulation transfer function (MTF) of a broad variety of metrology instrumentation has been demonstrated. This technique is based on test samples structured as one-dimensional binary pseudo-random (BPR) sequences and two-dimensional BPR arrays (BPRAs). The inherent power spectral density of BPR gratings (sequences) and arrays has a deterministic white-noise-like character that allows direct determination of the MTF with uniform sensitivity over the entire spatial frequency range and field-of-view of an instrument. As such, the BPR samples satisfy the characteristics of a test standard: functionality, ease of specification and fabrication, reproducibility, and low sensitivity to manufacturing error. Here we discuss our recent developments directed to the optimization of the sample design, fabrication, application, and data processing procedures, suitable for thorough characterization of large aperture optical interferometers. Compared with the previous coded-aperture based design, the improved, 'highly randomized' BPRA pattern of the new test standard provides better accuracy and reliability of instrument MTF and aberration characterization, and enables operation optimization of large aperture optical interferometers. We describe the pattern generation algorithm and tests to verify the compliance to desired BPRA topography. The data acquisition and analysis procedures for different applications of the technique are also discussed.

Yashchuk, Valeriy V.↗

Magnetosphere Evolution and Precursor-driven Electromagnetic Signals in Merging Binary Neutron Stars

We detail new force-free simulations to investigate magnetosphere evolution and precursor electromagnetic (EM) signals from binary neutron stars. Our simulations fully follow a representative inspiral motion, capturing the intricate magnetospheric dynamics and their impact on EM outflows. We explore a range of stellar magnetic moment orientations and relative strengths, finding that the magnetospheres and Poynting flux evolution are strongly configuration dependent. The Poynting flux exhibits pulsations at twice the orbital frequency, 2Ω, and is highly anisotropic, following a power-law dependence on orbital frequency. The index ranges from 1 to 6, shaped by the intricate dynamics of the magnetospheres. Furthermore, we present the first computation of (1) the EM forces acting on the star surfaces, revealing the presence of torques that, for highly magnetized stars, could influence the orbital dynamics or break the crust; (2) the high-energy emission signals from these systems by adopting the established isolated pulsar theory. Assuming curvature radiation in the radiation-reaction limit, we find that photons could reach TeV–PeV energies in the last ∼ms for magnetic field strengths 10 10 –10 15 G. However, our analysis of single photon magnetic pair production suggests that these photons are unlikely to escape, with the MeV band emerging as a promising observational window for precursor high-energy emission. In this framework, we construct high-energy emission skymaps and light curves, exploring observational implications. Finally, we propose potential precursor radio emission and delayed afterglow echoes from magnetized outflows, which may contribute to late-time rebrightening in short gamma-ray bursts or to orphan afterglows.

79 ASTRONOMY AND ASTROPHYSICS↗

Sintering Behaviors of ZrC, NbC, and TaC Mono-and Binary Carbides

Nuclear Thermal Propulsion (NTP) has undergone development as an alternate in-space propulsion system to traditional chemical propulsion methods since the 1950s. In an NTP system, the energy released from fission in the core is utilized as the heat source to directly heat a propellant for propulsion, rather than chemical combustion in a traditional rocket. NTP has many desirable capabilities including flexible mission launch dates and reduced transit times due to it’s capability for high specific impulse. One of the main challenges with NTP systems is the structural integrity of the fuel. The fuel form required in the reactor core must withstand temperatures above 2700 Kelvin. Ceramic-metallic matrix fuel, ceramic-ceramic matrix fuel, and solid solution carbide fuels are the three strongest candidates for the extreme environments. Solid solution carbides have the potential to exhibit promising behavior as a fuel form in an NTP system. Of the multiple refractory metal carbides of interest, here we focus on zirconium carbide (ZrC), niobium carbide (NbC), and tantalum carbide (TaC). ZrC, NbC, and TaC powders were consolidated in monocarbide (ZrC, NbC, and TaC) and bi-carbide (ZrC-NbC, ZrC-TaC, NbC-TaC) forms using spark plasma sintering (SPS). In addition to the pure endpoint carbides, the examined compositions of the various bi-carbides ranged from 25-75 mol%. The sintering temperatures, pressures, and hold times were varied to determine the ideal sintering conditions. Grain size analysis, Archimedes’ density, scanning electron microscopy (SEM), X-ray diffraction (XRD), and energy dispersion spectroscopy (EDS) were used to determine and characterize the grain size, ideal density, porosity, phase stability, and chemical composition of each sample. The data from each sample was then used to generate a Master Sintering Curve (MSC) unique to each monocarbide or bi-carbide.

nuclear thermal propulsion↗

The information content of jet quenching and machine learning assisted observable design

Jets produced in high-energy heavy-ion collisions are modified compared to those in proton-proton collisions due to their interaction with the deconfined, strongly-coupled quark-gluon plasma (QGP). In this work, we employ machine learning techniques to identify important features that distinguish jets produced in heavy-ion collisions from jets produced in proton-proton collisions. We formulate the problem using binary classification and focus on leveraging machine learning in ways that inform theoretical calculations of jet modification: (i) we quantify the information content in terms of Infrared Collinear (IRC)-safety and in terms of hard vs. soft emissions, (ii) we identify optimally discriminating observables that are in principle calculable in perturbative QCD, and (iii) we assess the information loss due to the heavy-ion underlying event and background subtraction algorithms. We illustrate our methodology using Monte Carlo event generators, where we find that important information about jet quenching is contained not only in hard splittings but also in soft emissions and IRC-unsafe physics inside the jet. This information appears to be significantly reduced by the presence of the underlying event. We discuss the implications of this for the prospect of using jet quenching to extract properties of the QGP. Since the training labels are exactly known, this methodology can be used directly on experimental data without reliance on modeling. We outline a proposal for how such an experimental analysis can be carried out, and how it can guide future measurements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Symbolic regression development of empirical equations for diffusion in Lennard-Jones fluids

Symbolic regression (SR) with a multi-gene genetic program has been used to elucidate new empirical equations describing diffusion in Lennard-Jones (LJ) fluids. Some examples include equations to predict self-diffusion in pure LJ fluids and equations describing the finite-size correction for self-diffusion in binary LJ fluids. The performance of the SR-obtained equations was compared to that of both the existing empirical equations in the literature and to the results from artificial neural net (ANN) models recently reported. It is found that the SR equations have improved predictive performance in comparison to the existing empirical equations, even though employing a smaller number of adjustable parameters, but show an overall reduced performance in comparison to more extensive ANNs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Validating sequential Monte Carlo for gravitational-wave inference

Nested sampling (NS) is the preferred stochastic sampling algorithm for gravitational-wave inference for compact binary coalescences. It can handle the complex nature of the gravitational-wave likelihood surface and provides an estimate of the Bayesian model evidence. However, there is another class of algorithms that meets the same requirements, but has not been used for gravitational-wave analyses: sequential Monte Carlo (SMC), an extension of importance sampling that maps samples from an initial density to a target density via a series of intermediate densities. In this work, we validate a type of SMC algorithm, called persistent sampling (PS), for gravitational-wave inference. We consider a range of different scenarios including binary black holes and binary neutron stars and real and simulated data and show that PS produces results that are consistent with NS whilst being, on average, 2 times more efficient and 2.74 times faster. This demonstrates that PS is a viable alternative to NS that should be considered for future gravitational-wave analyses.

black hole mergers↗

The Orbital Design of Alpha Centauri Exoplanet Satellite (ACESat)

Exoplanet candidates discovered by Kepler are too distant for biomarkers to be detected with foreseeable technology. Alpha Centauri has high separation from other stars and is of close proximity to Earth, which makes the binary star system 'low hanging fruit' for scientists. Alpha Centauri Exoplanet Satellite (ACESat) is a mission proposed to Small Explorer Program (SMEX) that will use a coronagraph to search for an orbiting planet around one of the stars of Alpha Centauri. The trajectory design for this mission is presented here where three different trajectories are considered: Low Earth Orbit (LEO), Geosynchronous Orbit (GEO) and a Heliocentric Orbit. Uninterrupted stare time to Alpha Centauri is desirable for meeting science requirements, or an orbit that provides 90% stare time to the science target. The instrument thermal stability also has stringent requirements for proper function, influencing trajectory design.

Alpha Centauri↗