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At least 1,729 records · Page 96
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Machine Learning for Conservative-to-Primitive in Relativistic Hydrodynamics

The numerical solution of relativistic hydrodynamics equations in conservative form requires root-finding algorithms that invert the conservative-to-primitive variables map. These algorithms employ the equation of state of the fluid and can be computationally demanding for applications involving sophisticated microphysics models, such as those required to calculate accurate gravitational wave signals in numerical relativity simulations of binary neutron stars. This work explores the use of machine learning methods to speed up the recovery of primitives in relativistic hydrodynamics. Artificial neural networks are trained to replace either the interpolations of a tabulated equation of state or directly the conservative-to-primitive map. The application of these neural networks to simple benchmark problems shows that both approaches improve over traditional root finders with tabular equation-of-state and multi-dimensional interpolations. In particular, the neural networks for the conservative-to-primitive map accelerate the variable recovery by more than an order of magnitude over standard methods while maintaining accuracy. Neural networks are thus an interesting option to improve the speed and robustness of relativistic hydrodynamics algorithms.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Field Validation of Wake Steering Control with Wind Direction Variability: Preprint

Wake steering is a type of wind farm control strategy wherein upstream turbines are misaligned with the wind direction to redirect their wakes away from downstream turbines, increasing overall wind plant power. Wake steering is often analyzed assuming steady mean wind directions across the wind farm. However, in practice, the wind direction varies considerably over time because of large-scale weather phenomena. Wind direction variability causes wake steering controllers to increase power production less than predicted by steady state models, but more robust wake steering strategies can be designed that account for variable wind conditions. This paper compares the achieved yaw offsets and power gains from two 2-turbine wake steering experiments at a commercial wind farm with model predictions using the FLOw Redirection and Induction in Steady State (FLORIS) control-oriented model, assuming both fixed and variable wind directions. The impact of wind direction variability is modeled by including wind direction and yaw uncertainty in the FLORIS calculations. The field results match the trends predicted assuming wind direction variability. Specifically, the yaw offsets achieved in the intended control regions are lower than desired, resulting in less power gain, while a slight loss in power occurs for wind directions outside of the intended control region because of unintentional yaw misalignment. The agreement between the model and field results suggests that the wind direction variability model can be used to design wake steering controllers that are more robust to variable wind conditions present in the field.

17 WIND ENERGY↗
DOE OSTI2022

Holocene paleoclimate change in the western US: The importance of chronology in discerning patterns and drivers

We report sediment in lakes and meadows forms a powerful archive that can be used to reconstruct environmental change through time. Reconstructions of lake level, of chemical, biological, and hydrological conditions, and of surrounding vegetation provide detailed information about past climate conditions, both locally and regionally. Indeed, most of our current knowledge of centennial- to millennial-scale climate variability in the arid western United States, where information about past hydroclimate is particularly important, comes from such sediment-based reconstructions. The pressing need for robust, precise predictions of future conditions is a significant motivation for paleoclimate science, and current research questions frequently require Holocene reconstructions to be resolved at sub-centennial timescales. Increasingly, regional syntheses seek to identify synoptic-scale patterns similar to those defined from modern observations (seasonal, interannual, multi-decadal, etc.) or to compare with the output of climate model simulations. However, the age control on existing records, especially those more than about 20 years old, is often sufficient only for millennial-scale interpretation. Here we assess the age control for 84 published and unpublished records from lakes and meadows in the Great Basin, California, and desert southwest, and use Bayesian modeling to evaluate the 95% uncertainty ranges for the 42 best-dated records. In the Late Holocene, about half of the 42 records have <400-year mean uncertainty ranges; however, high-precision age control is especially critical for young records, used to develop an accurate understanding of a proxy’s response to known climate variations. In the Middle Holocene, records vary from 400 to >800-year mean uncertainty and records of the Early Holocene have 600- to >1400-year mean uncertainty ranges. We find that the largest control on modeled uncertainties is dating density, with at least 2 dates/kyr being optimal and suggest obtaining “range-finder” dates at the onset of a study to better predict the total number of dates needed for an adequate age model. Such a density avoids a commonly observed phenomenon of significant peaks in uncertainty arising in gaps between age control points. Analysis of the uncertainties associated with proxy shifts reveal that more than half are >400 years. Although such large uncertainties currently prevent sub-centennial interpretations in most cases, increased dating density, strategic use of limited funds (including budgeting for a 2 date/kyr minimum at the proposal stage), construction of age-depth models with Bayesian methods, and critical evaluation of chronological uncertainty will shed light on past climate variability at finer timescales, enhancing our understanding of global and regional drivers of western U.S. climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Conducting mechanisms of forming-free TiW/Cu{sub 2}O/Cu memristive devices

P-type Cu{sub 2}O is a promising CMOS-compatible candidate to fabricate memristive devices for next-generation memory, logic and neuromorphic computing. In this letter, the microscopic switching and conducting mechanisms in TiW/Cu{sub 2}O/Cu memristive devices have been thoroughly investigated. The bipolar resistive switching behaviors without an electro-forming process are ascribed to the formation and rupture of the conducting filaments composed of copper vacancies. In the low resistive state, the transport of electrons in the filaments follows Mott's variable range hopping theory. When the devices switch back to high resistive state, the coexistence of Schottky emission at the Cu/Cu{sub 2}O interface and electron hopping between the residual filaments is found to dominate the conducting process. Our results will contribute to the further understanding and optimization of p-type memristive materials.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1972

Hill-climbing controller for plants with any dynamics and rapid drifts.

Description of extremal control systems which utilize extrapolation procedures to optimize plant operations or processes involving any kind of dynamics before and after the static extremal characteristic. The optimization sequence starts with an 'action of drift detection' - i.e., a measurement of the drift rate of the output. Then a 'search action' is initiated which consists of stepped shifts of the input variable; the output response due to these changes in the input variable is evaluated in the light of the predicted shift and is used to provide information on the shape of the extremal characteristic at the actual operating point. The position of the extremum is estimated by a parabolic extrapolation of the shape of the characteristic from the operating point to the extremum. During the 'control action' the operating point is adjusted to the position corresponding to the predicted optimum of the process by first applying a forcing step to the input variable to obtain a quicker response at the beginning of the transient state, and then applying a definitive step to bring the input variable back to the position which corresponds to the predicted extremum. A 'rest time' may be introduced before starting the next optimization sequence.

Jacob, H. G.↗
DOE OSTI2017

Diagnosing Cloud Biases in the GFDL AM3 Model With Atmospheric Classification

In this paper, we define a set of 21 atmospheric states, or recurring weather patterns, for a region surrounding the Atmospheric Radiation Measurement Program's Southern Great Plains site using an iterative clustering technique. The states are defined using dynamic and thermodynamic variables from reanalysis, tested for statistical significance with cloud radar data from the Southern Great Plains site, and are determined every 6 h for 14 years, creating a time series of atmospheric state. The states represent the various stages of the progression of synoptic systems through the region (e.g., warm fronts, warm sectors, cold fronts, cold northerly advection, and high-pressure anticyclones) with a subset of states representing summertime conditions with varying degrees of convective activity. We use the states to classify output from the NOAA/Geophysical Fluid Dynamics Laboratory AM3 model to test the model's simulation of the frequency of occurrence of the states and of the cloud occurrence during each state. The model roughly simulates the frequency of occurrence of the states but exhibits systematic cloud occurrence biases. Comparison of observed and model-simulated International Satellite Cloud Climatology Project histograms of cloud top pressure and optical thickness shows that the model lacks high thin cloud under all conditions, but biases in thick cloud are state-dependent. Frontal conditions in the model do not produce enough thick cloud, while fair-weather conditions produce too much. Finally, we find that increasing the horizontal resolution of the model improves the representation of thick clouds under all conditions but has little effect on high thin clouds. However, increasing resolution also changes the distribution of states, causing an increase in total cloud occurrence bias.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1995

Wind Variability of B Supergiants: The Rapid Rotator HD 64760 (B0.5 Ib) - No. 1

We present the results of a 6 day time series of observations of the rapidly rotating B0.5 Ib star HD 64760. We point out several reasons why such intermediate luminosity B supergiants are ideal targets for wind variability studies and then present our results that show the following: continuous wind activity throughout the 6 day run with the wind never in steady state for more than a few hr; wind variability very near nu = 0 km sec(exp -1) in the resonance lines from the lower ionization stages (Al III and C II); a distinct correlation between variability in the Si III ; lambda(lambda)1300 triplets, the strong C III (lambda)1247 singlet, and the onset of extremely strong wind activity, suggesting a connection between photospheric and wind activity; long temporal coherence in the behavior of the strong absorption events; evidence for large-scale spatial coherence, implied by a whole scale, simultaneous weakening in the wind absorption over a wide range in velocities; and ionization variability in the wind accompanying the largest changes in the absorption strengths of the wind lines. In addition, modeling of the wind lines provides the following information about the state the wind in HD 64760. The number of structures on the portion of a constant velocity surface occulting the stellar disk at a particular time must be quite small, while the number on the entire constant velocity surface throughout the wind must be large. The escape probability at low velocity is overestimated by a normal beta approx. 1 velocity law, perhaps due to the presence of low-velocity shocks deep in the wind or a shallow velocity gradient at low velocity. Estimates of the ionization structure in the wind indicate that the ionization ratios are not those expected from thermal equilibrium wind models or from an extrapolation of previous O star results. The large observed q(N V)/q(Si IV) ratio is almost certainly due to distributed X-rays, but the level of ionization predicted by distributed X-ray wind models is inconsistent with the predicted mass-loss rate. Thus, it is impossible to reconcile the observed ionization ratios and the predicted mass-loss rate within the framework of the available models.

Massa, Derck↗
DOE OSTI2017

Rapid Gamma-Ray Variability of NGC 1275

We report on a detailed analysis of the γ-ray light curve of NGC 1275 using the Fermi Large Area Telescope data accumulated during 2008–2017. Major γ-ray flares were observed in 2015 October and 2016 December/2017 January when the source reached a daily peak flux of (2.21±0.26)×10{sup −6} photon cm{sup −2} s{sup −1}, achieving a flux of (3.48±0.87)×10{sup −6} photon cm{sup −2} s{sup −1} within 3 hr, which corresponds to an apparent isotropic γ-ray luminosity of ≃3.84×10{sup 45} erg s{sup −1}. The most rapid flare had an e-folding time as short as 1.21±0.22 hr, which had never been previously observed for any radio galaxy in γ-ray band. Also, γ-ray spectral changes were observed during these flares: in the flux versus photon index plane, the spectral evolution follows correspondingly a counterclockwise and a clockwise loop inferred from the light curve generated by an adaptive binning method. On 2016 December 30 and 2017 January 1 the X-ray photon index softened (Γ{sub X}≃1.75--1.77) and the flux increased nearly ∼3 times as compared with the quiet state. The observed hour-scale variability suggests a very compact emission region (R{sub γ}⩽5.22×10{sup 14} (δ/4) cm), implying that the observed emission is most likely produced in the subparsec-scale jet if the entire jet width is responsible for the emission. During the active periods, the γ-ray photon index hardened, shifting the peak of the high-energy spectral component to >GeV, making it difficult to explain the observed X-ray and γ-ray data in the standard one-zone synchrotron self-Compton model.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1980

Baroclinic instability with variable gravity: A perturbation analysis

Solutions for a quasigeostrophic baroclinic stability problem in which gravity is a function of height were obtained. Curvature and horizontal shear of the basic state flow were omitted and the vertical and horizontal temperature gradients of the basic state were taken as constant. The effect of a variable dielectric body force, analogous to gravity, on baroclinic instability for the design of a spherical, baroclinic model for Spacelab was determined. Such modeling could not be performed in a laboratory on the Earth's surface because the body force could not be made strong enough to dominate terrestrial gravity. A consequence of the body force variation and the preceding assumptions was that the potential vorticity gradient of the basic state vanished. The problem was solved using a perturbation method. The solution gives results which are qualitatively similar to Eady's results for constant gravity; a short wavelength cutoff and a wavelength of maximum growth rate were observed. The averaged values of the basic state indicate that both the wavelength range of the instability and the growth rate at maximum instability are increased. Results indicate that the presence of the variable body force will not significantly alter the dynamics of the Spacelab experiment. The solutions are also relevant to other geophysical fluid flows where gravity is constant but the static stability or Brunt-Vaisala frequency is a function of height.

Giere, A. C.↗
NASA NTRS2018

Recent Updates in the SMAP Level-4 Soil Moisture Algorithm

The NASA Soil Moisture Active Passive (SMAP) mission generates, among other data sets, the Level-4 Soil Moisture (L4_SM) product. The L4_SM data are published with a mean latency of ~2.5 days from the time of observation and provide global, three-hourly, 9-km resolution estimates of surface and root-zone soil moisture and related land surface states and fluxes. The L4_SM algorithm is based on the assimilation of SMAP radiometer brightness temperature observations into the NASA Catchment land surface model using a spatially distributed ensemble Kalman filter (EnKF). In 2018, the L4_SM algorithm was upgraded from Version 3 to Version 4. Underlying the new version is a revised modeling system that includes improved input parameter datasets for land cover, topography, and vegetation height that are based on recent, high-quality, space-borne remote sensing observations. Additionally, SMAP Level-2 soil moisture retrievals and in situ soil moisture measurements were used to calibrate a particular Catchment model parameter that governs the recharge of surface soil moisture from below under non-equilibrium conditions, which brings the model's surface soil moisture more in line with the SMAP Level-2 and in situ soil moisture. Moreover, the calibration of the assimilated SMAP brightness temperatures changed substantially from Version 3 to Version 4, and the "catchment deficit" model variable was removed from the EnKF state vector to avoid degrading the model's groundwater estimates.Considerable effort went into the version upgrade, creating an expectation that the new version is improved over the old version. Indeed, some aspects of the new version are clearly better. However, other aspects are not. In this presentation we summarize the skill of the new and old versions vs. independent in situ measurements and in terms of data assimilation diagnostics, including, for example, the statistics of the (soil moisture) analysis increments and the observation-minus-forecast (brightness temperatures) residuals. We share our experience with trying to improve to the L4_SM product and the lessons learned from the effort.

Reichle, Rolf H.↗
DOE OSTI2022

Evidence of a room-temperature quantum spin Hall edge state in a higher-order topological insulator

Room-temperature realization of macroscopic quantum phases is one of the major pursuits in fundamental physics. The quantum spin Hall phase is a topological quantum phase that features a two-dimensional insulating bulk and a helical edge state. Here we use vector magnetic field and variable temperature based scanning tunnelling microscopy to provide micro-spectroscopic evidence for a room-temperature quantum spin Hall edge state on the surface of the higher-order topological insulator Bi 4 Br 4 . Here, we find that the atomically resolved lattice exhibits a large insulating gap of over 200 meV, and an atomically sharp monolayer step edge hosts an in-gap gapless state, suggesting topological bulk–boundary correspondence. An external magnetic field can gap the edge state, consistent with the time-reversal symmetry protection inherent in the underlying band topology. We further identify the geometrical hybridization of such edge states, which not only supports the Z 2 topology of the quantum spin Hall state but also visualizes the building blocks of the higher-order topological insulator phase. Our results further encourage the exploration of high-temperature transport quantization of the putative topological phase reported here.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2016

Iron-bound organic carbon in forest soils: quantification and characterization

Abstract. Iron oxide minerals play an important role in stabilizing organic carbon (OC) and regulating the biogeochemical cycles of OC on the earth surface. To predict the fate of OC, it is essential to understand the amount, spatial variability, and characteristics of Fe-bound OC in natural soils. In this study, we investigated the concentrations and characteristics of Fe-bound OC in soils collected from 14 forests in the United States and determined the impact of ecogeographical variables and soil physicochemical properties on the association of OC and Fe minerals. On average, Fe-bound OC contributed 37.8 % of total OC (TOC) in forest soils. Atomic ratios of OC : Fe ranged from 0.56 to 17.7, with values of 1–10 for most samples, and the ratios indicate the importance of both sorptive and incorporative interactions. The fraction of Fe-bound OC in TOC (fFe-OC) was not related to the concentration of reactive Fe, which suggests that the importance of association with Fe in OC accumulation was not governed by the concentration of reactive Fe. Concentrations of Fe-bound OC and fFe-OC increased with latitude and reached peak values at a site with a mean annual temperature of 6.6 °C. Attenuated total reflectance–Fourier transform infrared spectroscopy (ATR-FTIR) and near-edge X-ray absorption fine structure (NEXAFS) analyses revealed that Fe-bound OC was less aliphatic than non-Fe-bound OC. Fe-bound OC also was more enriched in 13C compared to the non-Fe-bound OC, but C ∕ N ratios did not differ substantially. In summary, 13C-enriched OC with less aliphatic carbon and more carboxylic carbon was associated with Fe minerals in the soils, with values of fFe-OC being controlled by both sorptive and incorporative associations between Fe and OC. Overall, this study demonstrates that Fe oxides play an important role in regulating the biogeochemical cycles of C in forest soils and uncovers the governing factors for the spatial variability and characteristics of Fe-bound OC.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Train Like a (Var)Pro: Efficient Training of Neural Networks with Variable Projection

Deep neural networks (DNNs) have achieved state-of-the-art performance across a variety of traditional machine learning tasks, e.g., speech recognition, image classification, and segmentation. The ability of DNNs to efficiently approximate high-dimensional functions has also motivated their use in scientific applications, e.g., to solve partial differential equations and to generate surrogate models. In this paper, we consider the supervised training of DNNs, which arises in many of the above applications. We focus on the central problem of optimizing the weights of the given DNN such that it accurately approximates the relation between observed input and target data. Devising effective solvers for this optimization problem is notoriously challenging due to the large number of weights, nonconvexity, data sparsity, and nontrivial choice of hyperparameters. To solve the optimization problem more efficiently, we propose the use of variable projection (VarPro), a method originally designed for separable nonlinear least-squares problems. Our main contribution is the Gauss--Newton VarPro method (GNvpro) that extends the reach of the VarPro idea to nonquadratic objective functions, most notably cross-entropy loss functions arising in classification. These extensions make GNvpro applicable to all training problems that involve a DNN whose last layer is an affine mapping, which is common in many state-of-the-art architectures. In our four numerical experiments from surrogate modeling, segmentation, and classification, GNvpro solves the optimization problem more efficiently than commonly used stochastic gradient descent (SGD) schemes. Finally, GNvpro finds solutions that generalize well, and in all but one example better than well-tuned SGD methods, to unseen data points.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2007

Uncertainty Analysis of Historical Hurricane Data

An analysis of variance (ANOVA) study was conducted for historical hurricane data dating back to 1851 that was obtained from the U. S. Department of Commerce National Oceanic and Atmospheric Administration (NOAA). The data set was chosen because it is a large, publicly available collection of information, exhibiting great variability which has made the forecasting of future states, from current and previous states, difficult. The availability of substantial, high-fidelity validation data, however, made for an excellent uncertainty assessment study. Several factors (independent variables) were identified from the data set, which could potentially influence the track and intensity of the storms. The values of these factors, along with the values of responses of interest (dependent variables) were extracted from the data base, and provided to a commercial software package for processing via the ANOVA technique. The primary goal of the study was to document the ANOVA modeling uncertainty and predictive errors in making predictions about hurricane location and intensity 24 to 120 hours beyond known conditions, as reported by the data set. A secondary goal was to expose the ANOVA technique to a broader community within NASA. The independent factors considered to have an influence on the hurricane track included the current and starting longitudes and latitudes (measured in degrees), and current and starting maximum sustained wind speeds (measured in knots), and the storm starting date, its current duration from its first appearance, and the current year fraction of each reading, all measured in years. The year fraction and starting date were included in order to attempt to account for long duration cyclic behaviors, such as seasonal weather patterns, and years in which the sea or atmosphere were unusually warm or cold. The effect of short duration weather patterns and ocean conditions could not be examined with the current data set. The responses analyzed were the storm latitude, longitude and intensity, as recorded in the data set, 24 or 120 hours beyond the current state. Several ANOVA modeling schemes were examined. Two forms of validation were used: 1) comparison with official hurricane prediction performance metrics and 2) cases studies conducted on hurricanes from the 2005 season, which were not included within the model construction and ANOVA assessment. In general, the ANOVA technique did not perform as well as the established official prediction performance metrics published by NOAA; still, the technique did remarkably well in this demonstration with a difficult data set and could probably be made to perform better with more knowledge of hurricane development and dynamics applied to the problem. The technique provides a repeatable prediction process that eliminates the need for judgment in the forecast.

Green, Lawrence L.↗
DOE OSTI2018

Low-Temperature Ionic Conductivity Enhanced by Disrupted Ice Formation in Polyampholyte Hydrogels

Here, the phase behavior of water in hydrogels has a broad impact on many health and energy applications. Our previous study showed that polyampholyte hydrogel has the potential to be used as an aqueous gel electrolyte in electrochemical storage devices at -30 °C due to enhanced low-temperature conductivity. In this study, we detail the impact polymer structure has on this enhanced conductivity, explaining this finding with a model charge-balanced polyampholyte, poly(4-vinylbenzenesulfonate- co -[3-(methacryloylam ino)propyl] trimethylammonium chloride), a hydrogel whose polymer and water structures are probed by variable-temperature SAXS, WAXS, and solid-state NMR spectroscopy. SAXS results at room temperature indicate a networked globule structure in the charge-balanced polyampholyte hydrogels. The globular radius of gyration is ~ 2.5 nm, whereas the globular size and its clustering structure are dependent on synthesis parameters. Variable-temperature SAXS data reveal a temperature-dependent structure evolution of the polyampholyte hydrogel. An interconnected globular network structure of polymer-rich phase at low temperature, observed by electron microscopy, is suggested to preserve ion-conducting channels of nonfrozen water molecules at low temperatures. This hypothesis is further supported by solid-state NMR spectroscopy. Together these findings provide macromolecular- and molecular-level insight that may be used to design gel electrolytes for enhanced low-temperature performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2000

Uncertainty Analysis of Decomposing Polyurethane Foam

Sensitivity/uncertainty analyses are necessary to determine where to allocate resources for improved predictions in support of our nation's nuclear safety mission. Yet, sensitivity/uncertainty analyses are not commonly performed on complex combustion models because the calculations are time consuming, CPU intensive, nontrivial exercises that can lead to deceptive results. To illustrate these ideas, a variety of sensitivity/uncertainty analyses were used to determine the uncertainty associated with thermal decomposition of polyurethane foam exposed to high radiative flux boundary conditions. The polyurethane used in this study is a rigid closed-cell foam used as an encapsulant. Related polyurethane binders such as Estane are used in many energetic materials of interest to the JANNAF community. The complex, finite element foam decomposition model used in this study has 25 input parameters that include chemistry, polymer structure, and thermophysical properties. The response variable was selected as the steady-state decomposition front velocity calculated as the derivative of the decomposition front location versus time. An analytical mean value sensitivity/uncertainty (MV) analysis was used to determine the standard deviation by taking numerical derivatives of the response variable with respect to each of the 25 input parameters. Since the response variable is also a derivative, the standard deviation was essentially determined from a second derivative that was extremely sensitive to numerical noise. To minimize the numerical noise, 50-micrometer element dimensions and approximately 1-msec time steps were required to obtain stable uncertainty results. As an alternative method to determine the uncertainty and sensitivity in the decomposition front velocity, surrogate response surfaces were generated for use with a constrained Latin Hypercube Sampling (LHS) technique. Two surrogate response surfaces were investigated: 1) a linear surrogate response surface (LIN) and 2) a quadratic response surface (QUAD). The LHS techniques do not require derivatives of the response variable and are subsequently relatively insensitive to numerical noise. To compare the LIN and QUAD methods to the MV method, a direct LHS analysis (DLHS) was performed using the full grid and timestep resolved finite element model. The surrogate response models (LIN and QUAD) are shown to give acceptable values of the mean and standard deviation when compared to the fully converged DLHS model.

Hobbs, Michael L.↗
NASA NTRS1977

X-ray sources in stellar winds

A model of a binary X-ray source immersed in a stellar wind from its companion star is considered. The geometry of the constant ionization and temperature contours in the model is discussed, and X-ray radiative transfer through the wind is described. The results are applied to make predictions about the presence and variability of iron K-shell fluorescence lines, to explain the light curve of Cen X-3 during a 'low' to 'high' state transition in July 1972, and to predict variability with orbital phase of resonance ultraviolet lines such as N V at 1238 and 1242 A from the binary system.

Hatchett, S.↗