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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.

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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

Prompt and Afterglow Emission Properties of Gamma-Ray Bursts with Spectroscopically Identified Supernovae

We present a detailed spectral analysis of the prompt and afterglow emission of four nearby GRBs (GRBs 980425, 030329, 031203, and 060218) that were spectroscopically found to be associated with type Ib/c supernovae. For each event, we investigated its spectral and luminosity evolution and estimated the total energy budget based on the broadband observations. We discuss the properties of the four events in comparison to general burst population, and infer the physical parameters involved in creation of these nearby GRB-SN events

Kaneko, Yuki↗
NASA NTRS2010

Physics of Solar Prominences: I-Spectral Diagnostics and Non-LTE Modelling

This review paper outlines background information and covers recent advances made via the analysis of spectra and images of prominence plasma and the increased sophistication of non-LTE (i.e. when there is a departure from Local Thermodynamic Equilibrium) radiative transfer models. We first describe the spectral inversion techniques that have been used to infer the plasma parameters important for the general properties of the prominence plasma in both its cool core and the hotter prominence-corona transition region. We also review studies devoted to the observation of bulk motions of the prominence plasma and to the determination of prominence mass. However, a simple inversion of spectroscopic data usually fails when the lines become optically thick at certain wavelengths. Therefore, complex

Labrosse, N.↗
NASA NTRS2020

Prognostics for Systems Health Management - Model and Hybrid Based Approaches. Where are We Heading?

To facilitate and solve the prediction problem, awareness of the current state and health of the system is key, since it is necessary to perform condition-based system health predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditional. In case of next generation electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. In order to tackle and solve the prediction problem, it is essential to have awareness of the current health state of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operational conditions and flight profiles for accurate estimation of end-of-discharge (EOD) for the batteries. Similar framework can be implemented to other complex systems and subsystems. Our research approach is to develop a system level health monitoring safety indicator which runs estimation and prediction algorithms to estimate remaining useful life predictions at system, subsystem swell as component levels. Given models of the current and future system behavior, a general approach of model-based prognostics is discussed as a solution to the prediction problem and further for decision making. Data driven prognostics approaches have been equally used with good results in the past, where respective approaches have their own challenges to tackle. This limits their applicability to complex real-world domains: (a) high complexity or incompleteness of physics-based models and (b) limited representativeness of the training dataset for data-driven models. With the advent of internet of things for data collection and increased use of ML algorithms, hybrid approaches are the next avenue to reduce the challenges and achieve better results. An hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, we use physics-based performance models to infer unobservable model parameters related to the system's components health solving a calibration problem.

Prognostics↗
NASA NTRS2019

A Joint NICER and XMM-Newton View of the "Magnificent" Thermally Emitting X-ray Isolated Neutron Star RX J1605.3+3249

Thermally emitting X-ray isolated neutron stars (NSs) represent excellent targets for testing cooling surface emission and atmosphere models, which are used to infer the physical parameters of the NS. Among the seven known members of this class, RXJ1605.3+3249 is the only one that still lacks confirmation of its spin period. Here we analyze NICER and XMM-Newton observations of RXJ1605.3+3249, in order to address its timing and spectral behavior. Contrary to a previous tentative detection, but in agreement with the recent work by Pires et al., we find no significant pulsation with a pulsed fraction higher than 1.3% (3σ) for periods above 150 ms. We also find a limit of 2.6% for periods above 2 ms, despite searches in different energy bands. The X-ray spectrum can be fit by either a double-blackbody model or by a single-temperature magnetized atmosphere model, both modified by a Gaussian absorption line at ∼0.44 keV. The origin of the absorption feature as a proton cyclotron line or as an atomic transition in the NS atmosphere is discussed. The predictions of the best-fit X-ray models extended to IR, optical, and UV bands, are compared with archival data. Our results are interpreted in the framework of a fallback disk scenario.

Christian Malacaria↗
NASA NTRSDate not supplied

Health Monitoring and Prognostics in Li-ion Batteries

Space applications need to overcome a very critical challenge of predicting remaining useful life of its critical systems/subsystems, with batteries being one of them. Batteries, power electronics conditioning system and motors and one of the most critical systems. Similarly in case of electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. To tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to accurately predict the future state of any system, it is required to possess knowledge of its current and future operations. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prior stated prediction problem. Two approaches are presented with battery prognostics application. The first approach presentation covers a physics based-modeling approach implemented for battery prognostics. Given models of the current and future system behavior, a general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making. A second hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems i.e. batteries is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Batteries↗
NASA NTRSDate not supplied

Health Monitoring and Prognostics in Li-ion Batteries

Space applications need to overcome a very critical challenge of predicting remaining useful life of its critical systems/subsystems, with batteries being one of them. Batteries, power electronics conditioning system and motors and one of the most critical systems. Similarly in case of electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. To tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to accurately predict the future state of any system, it is required to possess knowledge of its current and future operations. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prior stated prediction problem. Two approaches are presented with battery prognostics application. The first approach presentation covers a physics based-modeling approach implemented for battery prognostics. Given models of the current and future system behavior, a general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making. A second hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems i.e. batteries is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Battery Prognostics↗
NASA NTRS1992

Planetary camera observations of the central parsec of M32

New HST observations are reported which show that the M32 nucleus is extremely dense, with rho0 not less than 4 x 10 exp 6 solar masses/cu pc if M32 has a core with rc not greater than 0.37 pc, and rho0 greater than 3 x 10 exp 7 solar masses/cu pc if it instead has a central r exp -1/2 cusp. Limits on the M32 core radius are nearly three times smaller, and those on the central density at least four times greater, than values inferred from the best ground-based observations. A central relaxation that is almost an order of magnitude shorter than previous estimates is inferred from the new core parameters; a shorter stellar collision time is also inferred. It is argued that a starlight cusp surrounds the central black hole.

Lauer, Tod R.↗
NASA NTRS2008

The Mock LISA Data Challenge Round 3: New and Improved Sources

The Mock LISA Data Challenges are a program to demonstrate and encourage the development of data-analysis capabilities for LISA. Each round of challenges consists of several data sets containing simulated instrument noise and gravitational waves from sources of undisclosed parameters. Participants are asked to analyze the data sets and report the maximum information they can infer about the source parameters. The challenges are being released in rounds of increasing complexity and realism. Challenge 3. currently in progress, brings new source classes, now including cosmic-string cusps and primordial stochastic backgrounds, and more realistic signal models for supermassive black-hole inspirals and galactic double white dwarf binaries.

Baker, John↗
NASA NTRS1989

Interaction between the solar wind and the interstellar medium

The heliospheric and interstellar parameters of importance in the interaction between the solar wind and the ISM are discussed. The observationally inferred values of these parameters, including the uncertainties, are addressed, and the basic physical processes that are likely to be important in the interaction are examined theoretically. The theory is combined with observational information in an effort to develop the currently most likely picture of the heliosphere as it is shaped by the local ISM.

Holzer, Thomas E.↗
NASA NTRS1993

How reliable are coronal hole temperatures deduced from observations?

Given the importance of the temperature at the base of the corona for the modeling of the solar wind, we investigate the range of temperatures which have been deduced from remote measurements in coronal holes, within a heliocentric distance of 1.6 solar radii, and the accuracy to which these temperatures have been inferred. Results are presented from an analysis of EUV observations made simultaneously in three spectral lines at the limb in a polar coronal hole, with little contamination from quiet region emission. A temperature range of 7.8-9.3 x 10 exp 5 K is obtained, between 1.02 to 1.07 solar radii for the coronal hole, with a very different temperature range of 9.4 x 10 exp 5 - 1.2 x 10 exp 6 K for the quiet regions bordering it. Inhomogeneities within the coronal hole contribute to a 14 percent variation in inferred temperature. The elemental abundance, which is one of the parameters that influence the temperature inference, can in turn be significantly constrained when intensity ratios from three spectral lines are used.

Habbal, S. R.↗
NASA NTRS2017

Modular Spectral Inference Framework Applied to Young Stars and Brown Dwarfs

In practice, synthetic spectral models are imperfect, causing inaccurate estimates of stellar parameters. Using forward modeling and statistical inference, we derive accurate stellar parameters for a given observed spectrum by emulating a grid of precomputed spectra to track uncertainties. Spectral inference as applied to brown dwarfs re: Synthetic spectral models (Marley et al 1996 and 2014) via the newest grid spans a massive multi-dimensional grid applied to IGRINS spectra, improving atmospheric models for JWST. When applied to young stars(10Myr) with large starpots, they can be measured spectroscopically, especially in the near-IR with IGRINS.

K↗
NASA NTRS1990

Transition regions in solar system and astrophysical plasmas

A brief review is presented of basic particle and field characteristics of plasmas observed within the solar system, especially near transition regions, and their parameter ranges are compared with those inferred for stellar winds and the interstellar medium. Parameter ranges for solar system and astrophysical plasmas are found to have considerable overlap. In addition, astrophysics provides unique, global perspectives of large-scale systems, whereas solar-system space physics provides for direct quantitative testing of physical processes. Astrophysics and solar-system space physics studies thus have complementary and synergistic roles.

Eastman, Timothy E.↗
NASA NTRS1991

Use of aerosol microphysical measurements to model IR backscatter in support of GLOBE

Data on the concentration and composition of free tropospheric aerosol over the Pacific Ocean, collected during the GAMETAG program in 1977-1978 (Davis, 1980 and Patterson et al., 1980) are used to model values of aerosol optical extinction coefficients (sigma) at two wavelengths (0.55 and 1 micron) and values of volume backscatter coefficients (beta) at four wavelengths (1 micron, 9.11 microns, 9.25 microns, and 10.6 microns) and to investigate the relationship between these parameters. The mass concentrations inferred from the GAMETAG measurements with optical particle spectrometers agreed with the results of simultaneous chemical measurements. The study of the relationships among the optical parameters indicates that visible and near-visible values of beta and sigma may be useful in predicting 9.11- and 10.6-micron backscatter.

Patterson, Edward M.↗
NASA NTRS1989

Neutrinos from SN 1987A - Implications for cooling of the nascent neutron star and the mass of the electron antineutrino

Data on neutrinos from SN 1987A are compared here with parameterized models of the neutrino emission using a consistent and straightforward statistical methodology. The empirically measured detector background spectra are included in the analysis, and the data are compared with a much wider variety of neutrino emission models than was explored previously. It is shown that the inferred neutrino emission model parameters are strongly correlated. The analysis confirms that simple models of the neutrino cooling of the nascent neutron star formed by the SN adequately explain the data. The inferred radius and binding energy of the neutron star are in excellent agreement with model calculations based on a wide range of equations of state. The results also raise the upper limit of the electron antineutrino rest mass to roughly 25 eV at the 95 percent confidence level, roughly 1.5-5 times higher than found previously.

Loredo, Thomas J.↗
NASA NTRS1999

TMI Rain Rate Estimation Over Land and Ocean Utilizing Convective and Stratiform Discrimination

Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) radiometer brightness temperature data in the 85 GHz channel (T85) reveal distinct local minima in a regional map containing a Mesoscale Convective System (MCS). This is because of relatively small footprint size (approximately 5.5 km) and strong extinction properties in this channel of the TMI. A map of rain rate for that region, deduced from simultaneous measurements made by the Precipitation Radar (PR) on board the TRMM satellite, reveals that these T85 minima, produced by scattering, correspond to local PR rain maxima. Utilizing the PR rain rate map as a guide, we infer from TMI data the presence of three different kinds of thunderstorms or Cbs. They are young, mature, and decaying Cbs that have a scale of about 20 km on the average. Two parameters enable us to infer these three kinds of Cbs objectively: a) the magnitude of scattering depression deduced from local T85 minima and b) the mean horizontal gradient of T85 around such minima. Knowing the category of a given Cb, we can estimate the rain rate associated with it. Such estimation is done with the help of relationships linking T85 minimum to rain rate in each Cb type. Similarly, a weak background rain rate in all the areas where T85 is less than 260 K is deduced with another relationship linking T85 to rain rate. In our rain retrieval model, this background rain constitutes the stratiform rain where the Cbs are absent. Initially, these relationships are optimized or tuned utilizing the PR and TMI data of a few MCS events. After such tuning, the model is applied to independent MCS cases. The areal distribution of light (1-10 mm/hr), moderate (10-20 mm/hr), and intense (> 20 mm/hr) rain rates are retrieved satisfactorally. Accuracy in the estimates of the light, moderate and intense rain areas and the mean rain rates associated with such areas in these independent MCS cases is on the average about 15%. Taking advantage of this ability of our retrieval method, one could derive the latent heat input into the atmosphere over the 760 km wide swath of the TMI radiometer in the tropics.

Prabhakara, C.↗
NASA NTRS1983

A design methodology for nonlinear systems containing parameter uncertainty

In the present design methodology for nonlinear systems containing parameter uncertainty, a generalized sensitivity analysis is incorporated which employs parameter space sampling and statistical inference. For the case of a system with j adjustable and k nonadjustable parameters, this methodology (which includes an adaptive random search strategy) is used to determine the combination of j adjustable parameter values which maximize the probability of those performance indices which simultaneously satisfy design criteria in spite of the uncertainty due to k nonadjustable parameters.

Young, G. E.↗
NASA NTRS1982

Quantiles, parametric-select density estimation, and bi-information parameter estimators

A quantile-based approach to statistical analysis and probability modeling of data is presented which formulates statistical inference problems as functional inference problems in which the parameters to be estimated are density functions. Density estimators can be non-parametric (computed independently of model identified) or parametric-select (approximated by finite parametric models that can provide standard models whose fit can be tested). Exponential models and autoregressive models are approximating densities which can be justified as maximum entropy for respectively the entropy of a probability density and the entropy of a quantile density. Applications of these ideas are outlined to the problems of modeling: (1) univariate data; (2) bivariate data and tests for independence; and (3) two samples and likelihood ratios. It is proposed that bi-information estimation of a density function can be developed by analogy to the problem of identification of regression models.

Parzen, E.↗
NASA NTRS1990

A simulation survey of galaxy interactions

Many carefully selected samples of interacting galaxies have been observed extensively in attempts to clarify whether interaction produces activity in galaxies. Because the sample members represent a wide range of encounter parameters and times, one can then study whether there are correlations between observable encounter features and, for example, Seyfert activity. On the other hand, in theoretical studies, simulations typically deal with either time-consuming detailed modelling of single galaxy pairs or tracing a few model encounters over time. The authors extend the observational survey approach by combining it with a simulation survey. The authors are conducting a survey of model encounters, covering the most important encounter parameters over a wide range. Some parameters, such as companion structure and initial velocity, are demonstratably less important and can be ignored in a first pass. The parameter range must be richly enough sampled so that the authors can evaluate the uniqueness of the observable morphology and velocity structure of the resulting simulated pairs to diagnose unobservable companion orbit parameters. They are using a self-gravitating polar n-body code run on the Cray X-MP at the Alabama Supercomputer Network. For each simulation, the authors have stellar and gas distributions predicted over, typically, a billion years, along with information on gas motions within the disk and any material captured by the companion or lost to the system. Features of disturbed spiral galaxies are sensitive enough to time and encounter parameters so that a match of the simulation survey results to observations can be applied as starting points to infer unobservable orbital or system parameters in actual sample members. This should enable them to examine whether interesting observed properties (Seyfert activity, nuclear star-formation rate) are functions of unobservable dynamical properties which characterize each encounter. Any correlations (or lack of some expected ones) will provide strong clues as to how or whether these phenomena are related to interactions. Aside from its use with such observed samples, this survey should greatly speed determination of initial orbital parameters for more detailed subsequent simulations of individual systems.

Byrd, Gene G.↗