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NASA NTRS1981

Design studies of continuously variable transmissions for electric vehicles

Preliminary design studies were performed on four continuously variable transmission (CVT) concepts for use with a flywheel equipped electric vehicle of 1700 kg gross weight. Requirements of the CVT's were a maximum torque of 450 N-m (330 lb-ft), a maximum output power of 75 kW (100 hp), and a flywheel speed range of 28,000 to 14,000 rpm. Efficiency, size, weight, cost, reliability, maintainability, and controls were evaluated for each of the four concepts which included a steel V-belt type, a flat rubber belt type, a toroidal traction type, and a cone roller traction type. All CVT's exhibited relatively high calculated efficiencies (68 percent to 97 percent) over a broad range of vehicle operating conditions. Estimated weight and size of these transmissions were comparable to or less than equivalent automatic transmission. The design of each concept was carried through the design layout stage.

Parker, R. J.↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), has typically limited machine learning (ML) in space studies and further study of radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNAseq) data from 6 mouse liver GeneLab datasets (GLDS) with a total of 113 spaceflight and ground-control samples to determine top features relevant to spaceflight including the effect of radiation exposure. Data was normalized within each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. The top MRMR features were used to predict spaceflight vs. ground-control samples using a Random Forest (RF) classifier with 5-fold cross validation (CV). The ML-based gene sets were further compared against differential gene expression results from individual GLDS. CV training using the top 100 MRMR genes show averages of 86% accuracy and 0.95 AUC value on the validation set over 5 folds (Figure 1A). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 811 or 68 DEGs overlapping between at least 2 or 3 studies, respectively (Figure 1B). Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism. Set analysis between the MRMR features and the DEGs showed 60 or 8 genes overlapping with at least 1 or 2 studies, respectively. MRMR feature selection and ensemble ML methods (e.g. RF) improve performance relative to a Naïve Bayes classifier when NGS data sets are analyzed. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise ratio. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from RNASeq analysis. Non-intersecting sets introduce opportunity to explore spaceflight relevant genes and implementing ML methods across existing NGS datasets may overcome sample size limitations. ML coupled with existing analytical methods enhances understanding of disease by revealing common underlying pathways across datasets.

Machine Learning↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning (ML) Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRS1985

Vortex wake alleviation studies with a variable twist wing

Vortex wake alleviation studies were conducted in a wind tunnel and a water towing tank using a multisegmented wing model which provided controlled and measured variations in span load. Fourteen model configurations are tested at a Reynolds number of one million and a lift coefficient of 0.6 in the Langley 4- by 7-Meter Tunnel and the Hydronautics Ship Model Basin water tank at Hydronautics, Inc., Laurel, Md. Detailed measurements of span load and wake velocities at one semispan downstream correlate well with each other, with inviscid predictions of span load and wake roll up, and with peak trailing-wing rolling moments measured in the far wake. Average trailing-wing rolling moments are found to be an unreliable indicator of vortex wake intensity because vortex meander does not scale between test facilities and free-air conditions. A tapered-span-load configuration, which exhibits little or no drag penalty, is shown to offer significant downstream wake alleviation to a small trailing wing. The greater downstream wake alleviation achieved with the addition of spoilers to a flapped-wing configuration is shown to result directly from the high incremental drag and turbulence associated with the spoilers and not from the span load alteration they cause.

Holbrook, G. T.↗
NASA NTRS1959

A Dynamic Model Investigation of the Effect of a Sharp-Edge Vertical Gust on Blade Periodic Flapping Angles and Bending Moments of a Two-Blade Rotor

A two-blade rotor having a diameter of 4 feet and a solidity of 0.037 was subjected to sharp-edge vertical gusts while being operated at various forward speeds to study the effect of the gusts on the blade periodic bending moments and flapping angles. Variables studied included gust velocity, collective pitch angle, flapping hinge offset, and tip-speed ratio. Dimensionless coefficients are derived for the periodic components of the incremental changes in blade flapping angles and bending moments which arise when a rotor blade penetrates a sharp-edge gust. Mental changes in both the flapping angles and bending moments are essentially proportional to gust velocity, and the coefficients express the ratio of these increments to gust velccity. The results show that the flapping coefficient usually increases with an increase in collective pitch angle, is generally dependent on tip-speed ratio, and is essentially independent of the amount of flapping hinge offset. The bending-moment coefficient is also dependent on collective pitch angle and tip-speed ratio. Expected reductions in bending moments are realized by the use of flapping hinges, and further reductions in bending moments are achieved as the amount of flapping hinge offset is increased. Comparison of the experimental results of this investigation with limited available theoretical results shows substantial agreement but indicates that the assumption that the response of the rotor to a sharp-edge gust is independent of the collective pitch angle prior to gust entry is probably inadequate.

McCarty, John Locke↗
NASA NTRS1935

Tests of 16 related airfoils at high speed

In order to provide information that might lead to the development of better propeller section, 13 related symmetrical airfoils were tested in the NACA high-speed wind tunnel for a study of the effect of thickness form on the aerodynamic characteristics. The thickness-form variables studies were the value of the maximum thickness, the position along the chord at which the maximum thickness occurs, and the value of the leading-edge radius. The tests were conducted through the low angle-of-attack range for speeds extending from 35 percent of that of sound to slightly in excess of the speed at which a compressibility burble, or breakdown of flow, occurs. The corresponding Reynolds number range is 350,000 to 750,000.

Stack, John↗
NASA NTRS1987

Logistic Risk Model for the Unique Effects of Inherent Aerobic Capacity on (+)G(sub z) Tolerance Before and After Simulated Weightlessness

Small sample size (n less than 1O) and inappropriate analysis of multivariate data have hindered previous attempts to describe which physiologic and demographic variables are most important in determining how long humans can tolerate acceleration. Data from previous centrifuge studies conducted at NASA/Ames Research Center, utilizing a 7-14 d bed rest protocol to simulate weightlessness, were included in the current investigation. After review, data on 25 women and 22 men were available for analysis. Study variables included gender, age, weight, height, percent body fat, resting heart rate, mean arterial pressure, Vo(sub 2)max and plasma volume. Since the dependent variable was time to greyout (failure), two contemporary biostatistical modeling procedures (proportional hazard and logistic discriminant function) were used to estimate risk, given a particular subject's profile. After adjusting for pro-bed-rest tolerance time, none of the profile variables remained in the risk equation for post-bed-rest tolerance greyout. However, prior to bed rest, risk of greyout could be predicted with 91% accuracy. All of the profile variables except weight, MAP, and those related to inherent aerobic capacity (Vo(sub 2)max, percent body fat, resting heart rate) entered the risk equation for pro-bed-rest greyout. A cross-validation using 24 new subjects indicated a very stable model for risk prediction, accurate within 5% of the original equation. The result for the inherent fitness variables is significant in that a consensus as to whether an increased aerobic capacity is beneficial or detrimental has not been satisfactorily established. We conclude that tolerance to +Gz acceleration before and after simulated weightlessness is independent of inherent aerobic fitness.

Ludwig, David A.↗
NASA NTRS2007

Convective-Stratiform Precipitation Variability at Seasonal Scale from Eight Years of TRMM Observations: Implications for Multiple Modes of Diurnal Variability

This study investigated the variability of convective and stratiform rainfall from eight years (1998-2005) of Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) and TRMM Microwave Imager (TMI) measurements--focusing on seasonal diurnal variability. The main scientific goals are: (1) to understand the climatological variability of these two dominant forms of precipitation across the four cardinal seasons and over continents and oceans separately, and (2) to understand how differences in convective and stratiform rainfall variations ultimately determine how diurnal variability of total rainfall is modulated into multiple modes. There are distinct day-night differences for both convective and stratiform rainfall. Oceanic (continental) convective rainfall is up to 25% (50%) greater during nighttime (daytime) than daytime (nighttime). Seasonal variability of convective rainfall's day-night difference is relatively small, while stratiform rainfall exhibits very apparent day-night variations with a seasonal variability of these variations. There are consistent late evening diurnal peaks without obvious seasonal variations over ocean for convective, stratiform, and total rainfall. Over continents, convective and total rainfall exhibit a consistent dominant afternoon peak with little seasonal variability--with a late evening secondary peal exhibiting seasonal variation. Stratiform rainfall over continents shows a consistent strong late evening peak with a weak afternoon peak--with the afternoon mode undergoing seasonal variability. Therefore, the diurnal characteristics of stratiform rainfall control the afternoon secondary maximum of oceanic rainfall and the late evening secondary peak of continental rainfall. Even at seasonal-regional scale spatially or an interannual global scale temporally, the secondary mode can become very pronounced, but on an intermittent basis. Overall, the results demonstrate the importance of partitioning total rainfall into convective and stratiform components and that diurnal modes largely arise from distinct diurnal stratiform variations modulating convective variations.

Yang, Song↗
NASA NTRS2016

Rotational Properties of the Haumea Family Members and Candidates: Short-Term Variability

Haumea is one of the most interesting and intriguing trans-Neptunian objects (TNOs). It is a large, bright, fast rotator, and its spectrum indicates nearly pure water ice on the surface. It has at least two satellites and a dynamically related family of more than 10 TNOs with very similar proper orbital parameters and similar surface properties. The Haumean family is the only one currently known in the trans-Neptunian belt. Various models have been proposed, but the formation of the family remains poorly understood. In this work, we have investigated the rotational properties of the family members and unconfirmed family candidates with short-term variability studies, and report the most complete review to date. We present results based on five years of observations and report the short-term variability of five family members, and seven candidates. The mean rotational periods, from Maxwellian fits to the frequency distributions, are 6.27 +/- 1.19 hr for the confirmed family members, 6.44 +/- 1.16 hr for the candidates, and 7.65 +/- 0.54 hr for other TNOs (without relation to the family). According to our study, there is a possibility that Haumea family members rotate faster than other TNOs, however, the sample of family member is still too limited for a secure conclusion. We also highlight the fast rotation of 2002 GH(sub 32). This object has a 0.36 +/- 0.02 mag amplitude lightcurve and a rotational period of about 3.98 hr. Assuming 2002 GH(sub 32) is a triaxial object in hydrostatic equilibrium, we derive a lower limit to the density of 2.56 g cm(exp -3). This density is similar to Haumea's and much more dense than other small TNO densities.

Thirouin, Audrey↗
NASA NTRS1989

Time variability of the X-ray sources in M33

The results of a variability study of the X-ray sources detected by the Einstein Observatory in the galaxy M33 are reported. Two of the 15 known sources are variable above the 99.73 percent confidence level. The light curve of one of these sources, M33 X-7, exhibits a variability pattern of high and low states, suggesting an eclipsing binary X-ray source. Such a finding would be the first identification of a close accreting binary system with an X-ray source in an external galaxy other than the Magellanic Clouds. The data suggest a binary period of 1.7857 day and an eclipse duration of about 0.4 day. The nuclear source M33 X-8 varies only in the softest part of the spectrum. The observations suggest a rapid variability and show a rapid flare with a rise time shorter than three days together with longer timescale variability.

Peres, G.↗
NASA NTRS2012

GeV/TeV Cooperation: Ongoing Strategies

Although many of their scientific interests overlap, current GeV and reV telescopes have many properties that show strong contrasts. Field of view, duty cycle, speed of analysis, photon counting statistics, background, and data release policies differ markedly. The challenge for cooperative studies has been to make the best use of complementary properties. One example is GeV discovery and monitoring surveys for AGN coupled with rev time variability studies. Another is GeV spectral measurements for Galactic sources joined with reV spatial resolution. In many cases, sharing of information has been a positive factor, and continuing to explore ways to exchange results and ideas will help to maximize the scientific return from all gamma-ray observatories.

Thompson, David J.↗
NASA NTRS2017

Climate Limits Across Space and Time on European Forest Structure

The impact climate has on forests has been extensively studied. However, the large scale effect climate has on forest structures, such as average diameters, heights and basal area are understudied in a spatially explicit manner. The limits, tipping points and thresholds that climate places on forest structures dictate the services a forest may provide, the vulnerability of a forest to mortality and the potential value of the timber there within. The majority of current research either investigates climate impacts on forest pools and fluxes, on a tree physiological scale or on case studies that are used to extrapolate results and potential impacts. A spatially explicit study on how climate affects forest structure over a large region would give valuable information to stakeholders who are more concerned with ecosystem services that cannot be described by pools and fluxes but require spatially explicit information - such as biodiversity, habitat suitability, and market values. In this study, we quantified the limits that climate (maximum, minimum temperature and precipitation) places on 3 forest structures, diameter at breast height, height, and basal area throughout Europe. Our results show clear climatic zones of high and low upper limits for each forest structure variable studied. We also spatially analyzed how climate restricts the potential bio-physical upper limits and creates tipping points of each forest structure variable and which climate factors are most limiting. Further, we demonstrated how the climate change has affected 8 individual forests across Europe and then the continent as a whole. We find that diameter, height and basal area are limited by climate in different ways and that areas may have high upper limits in one structure and low upper limits in another limited by different climate variables. We also found that even though individual forests may have increased their potential upper limit forest structure values, European forests as a whole have lost, on average, 5.0%, 1.7% and 6.5% in potential mean forest diameter, height and basal area, respectively.

Moreno, Adam La Salle↗
NASA NTRS2008

AIRS Data Service at NASA Goddard Earth Sciences Data and Information Services (GES DISC) and Its Application to Climate Change Study

The Atmospheric Infrared Sounder (AIRS) instrument suite is designed to observe and characterize the entire atmospheric column from the surface to the top of the atmosphere in terms of surface emissivity and temperature, atmospheric temperature and humidity profiles, cloud amount and height, and the spectral outgoing infrared radiation on a global scale. The AIRS Data Support Team at the GES DISC provides data support to assist others in understanding, retrieving and extracting information from the AIRS/AMSU/HSB data products. Because a number of years has passed since its operation started, the amount of data has reached a certain level of maturity where we can address the climate change study utilizing AIRS data, In this presentation we will list various service we provide and to demonstrate how to utilize/apply the existing service to long-term and short-term variability study.

Won, Young-In↗
NASA NTRS1978

Automatic Welding of Stainless Steel Tubing

To determine if the use of automatic welding would allow reduction of the radiographic inspection requirement, and thereby reduce fabrication costs, a series of welding tests were performed. In these tests an automatic welder was used on stainless steel tubing of 1/2, 3/4, and 1/2 inch diameter size. The optimum parameters were investigated to determine how much variation from optimum in machine settings could be tolerate and still result in a good quality weld. The process variables studied were the welding amperes, the revolutions per minute as a function of the circumferential weld travel speed, and the shielding gas flow. The investigation showed that the close control of process variables in conjunction with a thorough visual inspection of welds can be relied upon as an acceptable quality assurance procedure, thus permitting the radiographic inspection to be reduced by a large percentage when using the automatic process.

Clautice, W. E.↗
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↗
NASA NTRS1994

Two very luminous variable X-ray sources in M82

We report on a variability study of X-ray sources in the starburst galaxy M82, based on ROSAT and Einstein High-Resolution Imager observations. In particular, we concentrate our analysis on two bright sources which exhibit significant variability. The brightest source in M82 is located in the central core of the galaxy, is variable within individual ROSAT observations, and indication are that it might have varied between the Einstein and the ROSAT observations. It is the most luminous X-ray binary candidate yet known. The other source is located outside the crowded central region and was very bright in the Einstein observation, but was not detected by ROSAT, despite the larger effective area of the instrument and the much longer exposure. The detection of variability poses strong constraints on the X-ray luminosity of individual components of the X-ray sources. Even if both sources are radiating at the Eddington limit, their mass would be at least several solar masses, making them candidates for extragalactic black holes.

Collura, A.↗