SEARCH · Engineering Papers

Results for “binary uncertainty”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 181 records · Page 10

All sources NASA NTRS DOE OSTI

NASA NTRS1994

Radial velocity measurements of the chromospherically-active stars (2): HD 28591 = V492 Per

From two sets of the spectroscopic observations covering a ten year period we have obtained 59 radial velocities of the chromospherically-active star HD 28591 = V492 Per. It is a G9III single-lined spectroscopic binary with a period of 21.2910 days and a circular orbit. The upsilon sin i of 24.6 km/sec, results in a minimum radius 10.3 solar radii. We estimate a distance of 165 +/- 40 pc and an orbital inclination of 65 +/- 25 degrees. The secondary is probably a mid to late-type K dwarf. The star is brighter than the limiting magnitude of the Bright Star Catalogue. The mean photometric and the orbital periods are identical within their uncertainties. Since the star fills a significant fraction of its Roche lobe, about 62%, the photometric light curve may be the result of starspots and a modest ellipticity effect.

Dadonas, V.↗

DOE OSTI2023

Search for 22 Na in novae supported by a novel method for measuring femtosecond nuclear lifetimes

Classical novae are thermonuclear explosions in stellar binary systems, and important sources of 26 Al and 22 Na. While γ rays from the decay of the former radioisotope have been observed throughout the Galaxy, 22 Na remains untraceable. Its half-life (2.6 yr) would allow the observation of its 1.275 MeV γ-ray line from a cosmic source. However, the prediction of such an observation requires good knowledge of its nucleosynthesis. The 22 Na(p, γ) 23 Mg reaction remains the only source of large uncertainty about the amount of 22 Na ejected. Its rate is dominated by a single resonance on the short-lived state at 7785.0(7) keV in 23 Mg. Here, we propose a combined analysis of particle-particle correlations and velocity-difference profiles to measure femtosecond nuclear lifetimes. The application of this method to the study of the 23 Mg states, places strong limits on the amount of 22 Na produced in novae and constrains its detectability with future space-borne observatories.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS1985

The mass transfer rate in X1916-053 - It is driven by gravitational radiation?

A 50-minute period for a binary system harboring an X-ray burster would allow several alternatives for the mass-giving secondary, including an H-shell burning-plus-He degenerate core composite model. The burst properties of X1916-053 are presently used to argue against the He degenerate as well as the He main sequence solutions and to estimate whether, for any of the other solutions, the mass transfer rate could be consistent with that expected from gravitational radiation (GR). Within an uncertainty of a factor of 2, the transfer rate for the composite model solution is consistent with gravitational radiation, but enhancement by other mechanisms should be investigated.

Swank, J. H.↗

NASA NTRS2020

Improving Rain/No-Rain Detection Skill by Merging Precipitation Estimates from Different Sources

Rain/no-rain detection error is a key source of uncertainty in regional and global precipitation products that propagates into offline hydrological and land surface modeling simulations. Such detection error is difficult to evaluate and/or filter without access to high-quality reference precipitation datasets. For cases where such access is not available, this study proposes a novel approach for improved rain/no-rain detection. Based on categorical triple collocation (CTC) and a probabilistic framework, a weighted merging algorithm (CTC-M) is developed to combine noisy, but independent, precipitation products into an optimal binary rain/no-rain time series. Compared with commonly used approaches that directly apply the best parent product for rain/no-rain detection, the superiority of CTC-M is demonstrated analytically and numerically using spatially dense precipitation measurements over Europe. Our analysis also suggests that CTC-M is tolerant to a range of cross-correlated rain/no-rain detection errors and detection biases of the parent products. As a result, CTC-M will benefit global precipitation estimation by improving the representation of precipitation occurrence in gauge-based and multisource merged precipitation products.

Jianzhi Dong↗

NASA NTRS2010

Automated Detection of Clouds in Satellite Imagery

Many different approaches have been used to automatically detect clouds in satellite imagery. Most approaches are deterministic and provide a binary cloud - no cloud product used in a variety of applications. Some of these applications require the identification of cloudy pixels for cloud parameter retrieval, while others require only an ability to mask out clouds for the retrieval of surface or atmospheric parameters in the absence of clouds. A few approaches estimate a probability of the presence of a cloud at each point in an image. These probabilities allow a user to select cloud information based on the tolerance of the application to uncertainty in the estimate. Many automated cloud detection techniques develop sophisticated tests using a combination of visible and infrared channels to determine the presence of clouds in both day and night imagery. Visible channels are quite effective in detecting clouds during the day, as long as test thresholds properly account for variations in surface features and atmospheric scattering. Cloud detection at night is more challenging, since only courser resolution infrared measurements are available. A few schemes use just two infrared channels for day and night cloud detection. The most influential factor in the success of a particular technique is the determination of the thresholds for each cloud test. The techniques which perform the best usually have thresholds that are varied based on the geographic region, time of year, time of day and solar angle.

Jedlovec, Gary↗

DOE OSTI2021

Hierarchical Data Format for Nuclear Data Sensitivities

The SCALE code system includes capabilities for sensitivity and uncertainty (S/U) analysis as part of its TSUNAMI code suite. The sensitivity of a quantity of interest (for example, an application’s $k_{eff}$) to nuclear data is stored as a profile in a text-based file, which is known as a sensitivity data file (SDF). The sensitivity profile can be used to calculate uncertainties, correlation coefficients, and similarity indices. One of the goals of the present work was to seek general performance improvements in the TSUNAMI code suite, starting with the TSUNAMI-IP code for calculating similarity indices. Through profiling, it was found that reading the text-based sensitivity files was a performance bottleneck in the TSUNAMI-IP code. In a typical TSUNAMI-IP calculation, an application might be compared to thousands of benchmarks, thus requiring the reading of thousands of SDFs. Reading of binary-based data is generally faster than reading text-based data. Hierarchical Data Format 5 (HDF5) is a binary-based format that also benefits from being portable, and it can be inspected with nonproprietary tools. This paper describes an HDF5-based file format that has been introduced for SDFs.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

Fabrication of yttrium hydride for high-temperature moderator application

We report given the superior thermal stability and highly attainable hydrogen density, yttrium hydride is an excellent high-temperature moderator material in advanced thermal neutron spectrum reactors that require small core volumes. Large-scale, crack-free, bulk yttrium hydride is in high demand; however, fabrication of yttrium hydride is challenging and has not been demonstrated for nearly half century. The associated challenges are discussed herein. In response to these challenges, a hydriding system was designed and constructed at Oak Ridge National Laboratory and was used to successfully fabricate crack-free yttrium hydride in complex geometries at large scales. This was accomplished by precisely controlling the hydrogen’s partial pressure and the retort temperature, which was informed by the well-established thermodynamic properties of the binary H–Y system. Hydrogen content in as-fabricated hydride was determined by weight change, an approach which was considered reliable based on the use of ultra–high-purity yttrium, the absence of oxide phases up to levels detectable using x-ray diffraction (XRD), and the significant weight gain. Hydrogen distribution along one yttrium hydride rod was evaluated with XRD analysis on materials extracted from different locations on the rod. The results indicated a relatively homogeneous hydrogen distribution along the hydride rod, with <3% uncertainty in the fraction of the δ-phase hydride. In addition, significant efforts are being dedicated to establish a complete database summarizing the thermomechanical and physical properties of as-fabricated yttrium hydride and the irradiation response to facilitate its deployment as a high-temperature moderator in advanced nuclear reactors.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Black hole–neutron star mergers: The first mass gap and kilonovae

Observations of X-ray binaries indicate a dearth of compact objects in the mass range from ~2 –5 M ⊙ . The existence of this (first mass) gap has been used to discriminate between proposed engines behind core-collapse supernovae. From LIGO/Virgo observations of binary compact remnant masses, several candidate first mass gap objects, either neutron stars (NSs) or black holes (BHs), were identified during the O3 science run. Motivated by these new observations, we study the formation of BH-NS mergers in the framework of isolated classical binary evolution, using population synthesis methods to evolve large populations of binary stars (Population I and II) across cosmic time. We present results on the NS to BH mass ratios (q = M NS /M BH ) in merging systems, showing that although systems with a mass ratio as low as q = 0.02 can exist, typically BH-NS systems form with moderate mass ratios q = 0.1 –0.2. If we adopt a delayed supernova engine, we conclude that ~30% of BH-NS mergers may host at least one compact object in the first mass gap (FMG). Even allowing for uncertainties in the processes behind compact object formation, we expect the fraction of BH-NS systems ejecting mass during the merger to be small (from ~0.6 –9%). In our reference model, we assume: (i) the formation of compact objects within the FMG, (ii) natal NS/BH kicks decreased by fallback, (iii) low BH spins due to Tayler-Spruit angular momentum transport in massive stars. We find that ≲1% of BH-NS mergers will have any mass ejection and about the same percentage will produce kilonova bright enough to have a chance of being detected with a large (Subaru-class) 8 m telescope. Interestingly, all these mergers will have both a BH and an NS in the FMG.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1981

The Geothermal Probabilistic Cost Model with an Application to a Geothermal Reservoir at Heber, California

A financial accounting model that incorporates physical and institutional uncertainties was developed for geothermal projects. Among the uncertainties it can handle are well depth, flow rate, fluid temperature, and permit and construction times. The outputs of the model are cumulative probability distributions of financial measures such as capital cost, levelized cost, and profit. These outputs are well suited for use in an investment decision incorporating risk. The model has the powerful feature that conditional probability distribution can be used to account for correlations among any of the input variables. The model has been applied to a geothermal reservoir at Heber, California, for a 45-MW binary electric plant. Under the assumptions made, the reservoir appears to be economically viable.

Orren, L. H.↗

DOE OSTI2024

Bayesian inference of multi-messenger astrophysical data: Joint and coherent inference of gravitational waves and kilonovae

Multi-messenger observations of binary neutron star mergers can provide information on the neutron star’s equation of state (EOS) above the nuclear saturation density by directly constraining the mass-radius diagram. We present a Bayesian framework for joint and coherent analyses of multi-messenger binary neutron star signals. As a first application, we analyze the gravitational-wave GW170817 and the kilonova (kN) AT2017gfo data. These results are then combined with the most recent X-ray pulsar analyses of PSR J0030+0451 and PSR J0740+6620 to obtain new EOS constraints.We extend the bajes infrastructure with a joint likelihood for multiple datasets, support for various semi-analytical kN models, and numerical-relativity (NR)-informed relations for the mass ejecta, as well as a technique to include and marginalize over modeling uncertainties. The analysis of GW170817 used the TEOBResumS effective-one-body waveform template to model the gravitational-wave signal. The analysis of AT2017gfo used a baseline multicomponent spherically symmetric model for the kN light curves. Various constraints on the mass-radius diagram and neutron star properties were then obtained by resampling over a set of ten million parameterized EOSs, which was built under minimal assumptions (general relativity and causality).

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1991

Observational tests of convective core overshooting in stars of intermediate to high mass in the Galaxy

This study presents the results of 14 tests for the presence of convective overshooting in large convecting stellar cores for stars with masses of 4-17 solar masses which are members of detached close binary systems and of open clusters in the Galaxy. A large body of theoretical and observational data is scrutinized and subjected to averaging in order to minimize accidental and systematic errors. A conservative upper limit of d/HP less than 0.4 is found from at least four tests, as well as a tighter upper limit of d/HP less than 0.2 from one good test that is subject to only mild restrictions and is based on the maximum observed effective temperature of evolved blue supergiants. It is concluded that any current uncertainty about the distance scale for these stars is unimportant in conducting the present tests for convective core overshooting. The correct effective temperature scale for the B0.5-B2 stars is almost certainly close to one of the proposed hot scales.

Stothers, Richard B.↗

DOE OSTI2025

Kilonova Emissions from Neutron Star Merger Remnants: Implications for the Nuclear Equation of State

Multimessenger observations of binary neutron star mergers can provide valuable information on the nuclear equation of state (EOS). Here, we investigate the extent to which electromagnetic observations of the associated kilonovae allow us to place constraints on the EOS. For this, we use state-of-the-art three-dimensional general-relativistic magnetohydrodynamics simulations and detailed nucleosynthesis modeling to connect properties of observed light curves to properties of the accretion disk, and hence, the EOS. Using our general approach, we use multimessenger observations of GW170817/AT2017gfo to study the impact of various sources of uncertainty on inferences of the EOS. We constrain the radius of a 1.4M ⊙ neutron star to lie within 10.30 ≤ R 1.4 ≤ 13.0 km and the maximum mass to be M TOV ≤ 3.06M ⊙ .

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

High-resolution X-ray spectroscopy of the stellar wind in Vela X-1 during a flare

In this work, we present a ~130 ks observation of the prototypical wind-accreting, high-mass X-ray binary Vela X-1 collected with XMM-Newton at orbital phases between 0.12 and 0.28. A strong flare took place during the observation that allows us to investigate the reaction of the clumpy stellar wind to the increased X-ray irradiation. To examine the wind’s reaction to the flare, we performed both time-averaged and time-resolved analyses of the RGS spectrum and examined potential spectral changes. We focused on the high-resolution XMM-Newton RGS spectra and divided the observation into pre-flare, flare, and post-flare phases. We modeled the time-averaged and time-resolved spectra with phenomenological components and with the self-consistent photoionization models calculated via CLOUDY and XSTAR in the pre-flare phase, where strong emission lines due to resonant transitions of highly ionized ions are seen. In the spectra, we find emission lines corresponding to K-shell transitions in highly charged ions of oxygen, neon, magnesium, and silicon as well as radiative recombination continua (RRC) of oxygen. Additionally, we observe potential absorption lines of magnesium at a lower ionization stage and features identified as iron L lines. The, CLOUDY and XSTAR photoionization models provide contradictory results, either pointing towards uncertainties in theory or possibly a more complex multi-phase plasma, or both. We are able to demonstrate the existence of a plethora of variable narrow features, including the firm detection of oxygen lines and RRC that RGS enables to observe in this source for the first time. We show that Vela X-1 is an ideal source for future high-resolution missions, such as XRISM and Athena.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Fast and stable deep-learning predictions of material properties for solid solution alloys

We present a novel deep learning (DL) approach to produce highly accurate predictions of macroscopic physical properties of solid solution binary alloys and magnetic systems. The major idea is to make use of the correlations between different physical properties in alloy systems to improve the prediction accuracy of neural network (NN) models. We use multitasking NN models to simultaneously predict the total energy, charge density and magnetic moment. These physical properties mutually serve as constraints during the training of the multitasking NN, resulting in more reliable DL models because multiple physics properties are correctly learned by a single model. Two binary alloys, copper–gold (CuAu) and iron–platinum (FePt), were studied. Our results show that once the multitasking NN's are trained, they can estimate the material properties for a specific configuration hundreds of times faster than first-principles density functional theory calculations while retaining comparable accuracy. We used a simple measure based on the root-mean-squared errors to quantify the quality of the NN models, and found that the inclusion of charge density and magnetic moment as physical constraints leads to more stable models that exhibit improved accuracy and reduced uncertainty for the energy predictions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

NASA NTRS1995

On the nature of the companion to HD 114762

The results of a recent study of coplanarity tendencies between stellar-equatorial and binary-orbital planes are applied to the low-metal F9 V star HD 114762, for which a possible substellar companion was reported by Latham et al. (1989). High-resolution spectroscopy is performed on HD 114762 in order to extract its projected rotational velocity (v sin i). This is then combined with an expected rotational velocity determined via age-scaling, providing an estimate of the star's equatorial inclination, which then combined with the results of the coplanarity study provides an approximate indication of the companion's orbital inclination. Although the formal uncertainties in this process preclude an accurate estimate of the companion's inclination, the results suggest that the inclination is low, possibly low enough to force the companion's mass above the limit for hydrogen fusion. It is thus possible the companion may be nothing more exotic than a low-mass M star, as opposed to a brown dwarf. These results add support to a similar conclusion recently obtained by Cochran et al. (1991).

Hale, Alan↗

NASA NTRS2002

Electron Density Profiles of the Topside Ionosphere

The existing uncertainties about the electron density profiles in the topside ionosphere, i.e., in the height region from h,F2 to - 2000 km, require the search for new data sources. The ISIS and Alouette topside sounder satellites from the sixties to the eighties recorded millions of ionograms but most were not analyzed in terms of electron density profiles. In recent years an effort started to digitize the analog recordings to prepare the ionograms for computerized analysis. As of November 2001 about 350000 ionograms have been digitized from the original 7-track analog tapes. These data are available in binary and CDF format from the anonymous ftp site of the National Space Science Data Center. A search site and browse capabilities on CDAWeb assist the scientific usage of these data. All information and access links can be found at http://nssdc.gsfc.nasa.gov/space/isis/isis- status.htm1. This paper describes the ISIS data restoration effort and shows how the digital ionograms are automatically processed into electron density profiles from satellite orbit altitude (1400 km for ISIS-2) down to the F peak. Because of the large volume of data an automated processing algorithm is imperative. The TOPside Ionogram Scaler with True height algorithm TOPIST software developed for this task is successfully scaling - 70% of the ionograms. An < > is available to manually scale the more difficult ionograms. The automated processing of the digitized ISIS ionograms is now underway, producing a much-needed database of topside electron density profiles for ionospheric modeling covering more than one solar cycle.

Huang, Xue-Qin↗

DOE OSTI2022

Machine Learning Models for Binary Molecular Classification using VUV Absorption Spectra

Machine learning methods were combined with differential absorption spectroscopy measurements in the vacuum-ultraviolet region (5.167 – 9.920 eV) in order to develop predictive capabilities for inferring molecular structure from the spectra. Several types of species were analyzed and, for modeling purposes, were defined using a single classification: (1) alkane, (2) conjugation with oxygen (e.g. diacetyl, ethyl vinyl ether), (3) non-conjugated alkene (e.g. 1-butene, 1,4-cyclohexadiene), (4) oxygen-containing (e.g. 1-butanol, tetrahydrofuran), or (5) cyclic (e.g. cyclopentane, cyclohexanone). The latter molecular classification excluded cyclic ethers. Several modeling methods were employed in the analysis of 102 absorption spectra, 24 of which were measured for the first time. The primary objective was to identify suitable methods that enable accurate predictions of molecular structure classifications with minimized statistical uncertainties. Rather than identifying a single, unifying method to reliably predict molecular structure contributions to VUV absorption spectra, coordination is required among a particular method, the type of molecular structure detail (e.g. conjugation), and absorption region of interest. The latter is accomplished using a binning approach, wherein absorption regions of ~0.5 eV were utilized rather than the entire ~4.8 eV range. Photon energy binning enabled analysis of region-specific predictions of accuracy, precision, and recall. The outcome from the binning approach is that, rather than utilizing the entire spectrum, optimal determination of molecular structure using machine learning methods depends on the absorption region. Furthermore, the present work provides separate machine learning models for each molecular classification, which enables the identification of multi-functional species relevant to atmospheric chemistry and combustion chemistry, where isomer-resolved speciation is critical to understanding complex reaction networks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Simultaneous prediction of structural properties in epitaxially–grown GaN with quantum and conventional multi–output learning algorithms

Hundreds of GaN thin film crystal plasma–assisted molecular beam epitaxy synthesis experiment records spanning two decades were organized into a dataset correlating the growth experiment design parameters with discrete, binary determinations of crystallinity and surface morphology. Conventional data science techniques as well as both quantum and classical multi–output supervised machine learning algorithms were implemented to investigate the relationships between the operating parameter data and the structural figures of merit. Correlation coefficients, decision tree nodes, p–values, and SHAP values all support substrate temperature and gallium effusion cell conditions as being statistically significant for simultaneously influencing GaN crystallinity and surface morphology. Here, a conventional deep neural network learned best from the data, followed by a quantum–classical hybrid gradient boosting algorithm. When combined with calculations of uncertainty intervals based on VennAbers predictors, machine learning predictions of both structural properties show good agreement with results reported in published experimental literature.

36 MATERIALS SCIENCE↗