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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS1985

The physical implications of an isothermal model for the hot intracluster medium

HEAO-1 A-2 and Einstein Observatory IPC observations of 15 clusters of galaxies have been used to measure the isothermal beta-model parameters, and the parameters are compared with those derived from model fits to the X-ray images. The physical constraints imposed on this model by existing optical data, the implied gas mass, and the gas contribution to the binding cluster mass are addressed. Values of beta less than unity are obtained, consistent with results from X-ray imaging data. This is interpreted to imply that the gas extends out to 8-10 core radii, about 2-3 Mpc. The isothermal gas mass can be as large as 60 percent of the virial mass for some clusters. This contradicts the fundamental assumption that the cluster potential is determined by an unseen mass which is given by the King approximation to an isothermal sphere, and it is concluded that the isothermal model is a nonphysical model.

Henriksen, M. J.↗
NASA NTRS1990

Absolute wind measurements in the lower thermosphere of Venus using infrared heterodyne spectroscopy

The first absolute wind velocities above the Venusian cloud-tops were obtained using NASA/Goddard infrared heterodyne spectrometers at the NASA Infrared Telescope Facility (IRTF) and the McMath Solar Telescope. Beam-integrated Doppler displacements in the non-thermal emission core of (12)C(16)O2 10.33 micron R(8) sampled the line of sight projection of the lower thermospheric wind field (100 to 120 km). A field-usable Lamb-dip laser stabilization system, developed for spectrometer absolute frequency calibration to less than + or - 0.1 MHz, allowed S/N-limited line of sight velocity resolution at the 1 m/s level. The spectrometer's diffraction-limited beam (1.7 arc-second HPBW at McMath, 0.9 arc-second HPBW at IRTF), and 1 to 2 arc-second seeing, provided the spatial resolution necessary for circulation model discrimination. Qualitative analysis of beam-integrated winds provided definitive evidence of a dominant subsolar-antisolar circulation in the lower thermosphere. Beam-integrated winds were modelled with a 100x100 grid over the beam, incorporating beam spatial rolloff and across-the-beam gradients in non-thermal emission intensity, line of sight projection geometry, and horizontal wind velocity. Horizontal wind velocity was derived from a 2-parameter model wind field comprised of subsolar-antisolar and zonal components. Best-fit models indicated a dominant subsolar-antisolar flow with 120 m/s cross-terminator winds and a retrograde zonal component with a 25 m/s equatorial velocity. A review of all dynamical indicators above the cloud-tops allowed development of an integrated and self-consistent picture of circulation in the 70 to 200 km range.

Goldstein, Jeffrey J.↗
NASA NTRS1991

A mantle plume model for the Equatorial Highlands of Venus

The possibility that the Equatorial Highlands are the surface expressions of hot upwelling mantle plumes is considered via a series of mantle plume models developed using a cylindrical axisymmetric finite element code and depth-dependent Newtonian rheology. The results are scaled by assuming whole mantle convection and that Venus and the earth have similar mantle heat flows. The best model fits are for Beta and Atla. The common feature of the allowed viscosity models is that they lack a pronounced low-viscosity zone in the upper mantle. The shape of Venus's long-wavelength admittance spectrum and the slope of its geoid spectrum are also consistent with the lack of a low-viscosity zone. It is argued that the lack of an asthenosphere on Venus is due to the mantle of Venus being drier than the earth's mantle. Mantle plumes may also have contributed to the formation of some smaller highland swells, such as the Bell and Eistla regions and the Hathor/Innini/Ushas region.

Kiefer, Walter S.↗
NASA NTRS2014

Investigating the Differential Emission Measure and Energetics of Microflares with Combined SDO/AIA and RHESSI Observations

An important question in solar physics is whether solar microflares, the smallest currently observable flare events in X-rays, possess the same energetic properties as large flares. Recent surveys have suggested that microflares may be less efficient particle accelerators than large flares, and hence contribute less non-thermal energy, which may have implications for coronal heating mechanisms. We therefore explore the energetic properties of microflares by combining EUV and X-ray measurements. We present forward-fitting differential emission measure (DEM) analysis of 10 microflares. The fitting is constrained by combining, for the first time, high-temperature Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI) observations and flux data from the Solar Dynamics Observatory (SDO) Atmospheric Imaging Assembly (AIA). Two fitting models are tested for the DEM; a Gaussian distribution and a uniform DEM profile. A Gaussian fit proved unable to explain the observations for any of the studied microflares. However, 8 of 10 events studied were reasonably fit by a uniform DEM profile. Hence microflare plasma can be considered to be significantly multi-thermal, and may not be significantly peaked or contain resolvable fine structure, within the uncertainties of the observational instruments. The thermal and non-thermal energy is estimated for each microflare, comparing the energy budget with an isothermal plasma assumption. From the multi-thermal fits the minimum non-thermal energy content was found to average approximately 30% of the estimated thermal energy. By comparison, under an isothermal model the non-thermal and thermal energy estimates were generally comparable. Hence, multi-thermal plasma is an important consideration for solar microflares that substantially alters their thermal and non-thermal energy content.

Sun: flares↗
DOE OSTI2026

Estimation of Fission Product Transport Parameters for Cesium in the AGR-3/4 TRISO Fuel Experiment

A one-dimensional (1D) finite-element model of fission product transport in the AGR-3/4 experiment has been developed using the Multiphysics Object Oriented Simulation Environment (MOOSE) framework and implemented in the fuel performance code, BISON. The model resolves capsule-specific geometries, materials, and temperature histories and simulates radial migration of fission products from the fuel compact through the inner ring, outer ring, and into the sink ring. Model parameters governing diffusion and sorption were estimated for key fission products – cesium (Cs), and europium (Eu) – by simultaneously fitting modeled isotopic concentration profiles and total ring inventories to a post-irradiation experimental measurement. These data include gamma scanning, liquid scintillation for Sr-90, radial deconsolidation leach-burn-leach analysis, tomographic reconstructions, and destructive physical sampling. A mortar-based interfacial sorption framework was implemented to enforce physically consistent mass transfer and flux conservation across gas gaps. Two classes of parameter sets were derived: a least-squares best-fit, and a safety-oriented conservative-fit, what applies strong penalties for underprediction of sink inventories. Across all twelve capsules, the model successfully reproduces the dominant radial transport trends for Cs, Sr, with decreasing concentrations from the compact outward through successive rings. Cs behavior is captured most consistently, while strontium predictions reveal systematic trade-offs between compact accuracy and conservative sink-ring bounding. The results demonstrate that sink ring weighted calibration provides conservative, safety relevant bounds on low temperature fission product transport, but at the cost of underpredicting compact inventories for Sr isotopes. These discrepancies highlight the need for additional physics, including fast-slow diffusion model, incorporating trapping mechanism in the transport behavior. Overall, this work establishes a robust, capsule-specific modeling framework for AGR-3/4 fission product transport and provides a defensible basis for parameter selection in source-term and fuel performance analyses for high temperature gas-cooled reactors.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2001

Sympathetic restraint of respiratory sinus arrhythmia: implications for vagal-cardiac tone assessment in humans

Clinicians and experimentalists routinely estimate vagal-cardiac nerve traffic from respiratory sinus arrhythmia. However, evidence suggests that sympathetic mechanisms may also modulate respiratory sinus arrhythmia. Our study examined modulation of respiratory sinus arrhythmia by sympathetic outflow. We measured R-R interval spectral power in 10 volunteers that breathed sequentially at 13 frequencies, from 15 to 3 breaths/min, before and after beta-adrenergic blockade. We fitted changes of respiratory frequency R-R interval spectral power with a damped oscillator model: frequency-dependent oscillations with a resonant frequency, generated by driving forces and modified by damping influences. beta-Adrenergic blockade enhanced respiratory sinus arrhythmia at all frequencies (at some, fourfold). The damped oscillator model fit experimental data well (39 of 40 ramps; r = 0.86 +/- 0.02). beta-Adrenergic blockade increased respiratory sinus arrhythmia by amplifying respiration-related driving forces (P < 0.05), without altering resonant frequency or damping influences. Both spectral power data and the damped oscillator model indicate that cardiac sympathetic outflow markedly reduces heart period oscillations at all frequencies. This challenges the notion that respiratory sinus arrhythmia is mediated simply by vagal-cardiac nerve activity. These results have important implications for clinical and experimental estimation of human vagal cardiac tone.

NASA Discipline Cardiopulmonary↗
NASA NTRS2010

Focal Plane Geometry Characterization of the Kepler Mission

The Kepler Mission focal plane contains 42 charge-coupled device (CCD) photodetectors. Each CCD is composed of 2.2 million square pixels, 27 micrometers on a side, arranged in a grid of 2,200 columns by 1,044 rows. The science goals of the Kepler Mission require that the position of each CCD be determined with an accuracy of 0.1 pixels, corresponding to 2.7 micrometers or 0.4 seconds of arc, a level which is not achievable through pre-flight metrology. We describe a technique for determining the CCD positioning using images of the Kepler field of view (FOV) obtained in flight. The technique uses the fitted centroid row and column positions of 400 pre-selected stars on each CCD to obtain empirical polynomials which relate sky coordinates (right ascension and declination) to chip coordinates (row and column). The polynomials are in turn evaluated to produce constraints for a nonlinear model fit which directly determines the model parameters describing the location and orientation of each CCD. The focal plane geometry characterization algorithm is itself embedded in an iterative process which determines the focal plane geometry and the Pixel Response Function for each CCD in a self-consistent manner. In addition to the fully automated calculation, a person-in-the-loop implementation was developed to allow an initial determination of the geometry in the event of large misalignments, achieving a much looser capture tolerance for more modest accuracy and reduced automation.

ccd↗
DOE OSTI2022

Machine learning for impurity charge-state transition levels in semiconductors from elemental properties using multi-fidelity datasets

Quantifying charge-state transition energy levels of impurities in semiconductors is critical to understanding and engineering their optoelectronic properties for applications ranging from solar photovoltaics to infrared lasers. While these transition levels can be measured and calculated accurately, such efforts are time-consuming and more rapid prediction methods would be beneficial. Here, we significantly reduce the time typically required to predict impurity transition levels using multi-fidelity datasets and a machine learning approach employing features based on elemental properties and impurity positions. We use transition levels obtained from low-fidelity (i.e., local-density approximation or generalized gradient approximation) density functional theory (DFT) calculations, corrected using a recently proposed modified band alignment scheme, which well-approximates transition levels from high-fidelity DFT (i.e., hybrid HSE06). Further, the model fit to the large multi-fidelity database shows improved accuracy compared to the models trained on the more limited high-fidelity values. Crucially, in our approach, when using the multi-fidelity data, high-fidelity values are not required for model training, significantly reducing the computational cost required for training the model. Our machine learning model of transition levels has a root mean squared (mean absolute) error of 0.36 (0.27) eV vs high-fidelity hybrid functional values when averaged over 14 semiconductor systems from the II–VI and III–V families. As a guide for use on other systems, we assessed the model on simulated data to show the expected accuracy level as a function of bandgap for new materials of interest. Finally, we use the model to predict a complete space of impurity charge-state transition levels in all zinc blende III–V and II–VI systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1973

Estimation in a modified binomial distribution

Estimation techniques in a modified binomial distribution, developed to describe thunderstorm activity over a small area at Cape Kennedy, Florida, are compared. A compound model is also developed and compared with the original model. The minimum Chi square technique is compared with the maximum likelihood and method of moments techniques. The minimum Chi square technique, although useful in complicated models, compared poorly compared to the other techniques. The maximum likelihood and method of moments were comparable. The compound model fit better in every case based on a likelihood ratio test comparing the compound model with the modified binomial model using maximum likelihood estimators.

Carter, M. C.↗
NASA NTRS1989

The surface albedo distribution of Pluto

Two distinct configurations have been established for the surface albedo distribution of Pluto on the basis of a model fitting absolute photometry data obtained over the 1954-1986 period. The two sets of model parameters primarily differ in the equatorial spots; these generate the rotational lightcurve, while the polar caps yield the orbital lightcurve. While the two models predict similar event lightcurves through the end of 1987, they begin to markedly differ in 1988. Mutual event observations made on January 13, 1988 exhibit agreement with only one of the equatorial spot configurations, having one bright and one dark spot.

Buie, Marc W.↗
NASA NTRS2018

Impacts of Land Carbon Flux Variability on Variations in Atmospheric CO2

To better understand the role of atmospheric CO2 in the global carbon cycle, it is important to understand the factors that control its variability in time and space. Here we investigate these factors using a coupled land-atmosphere modeling system (the NASA GEOS-5 model) fitted with land carbon physics and atmospheric CO2 transport. To separate the influences of land carbon flux variability and atmospheric transport variability on the variability of CO2 concentrations, we conducted two carbon-fitted GEOS-5 AGCM simulations run in replay mode (a technique that guides the model's weather to match that of the MERRA-2 reanalysis): (i) a control simulation of 15-year carbon cycle dynamics and climate, and (ii) a simulation in which the climatological seasonal cycles of net biosphere production (NBP), as determined from diagnostics produced in the control experiment, are applied at the surface to the atmosphere instead. The impact of land flux variability on atmospheric CO2 variability is then isolated by subtracting the variability inherent in the second simulation (as induced, e.g., by strong El Nino conditions) from that in the control. These results are also presented in the context of supplemental simulations that have examined the impact of imposed drought in a free running AGCM simulation on carbon fluxes and atmospheric CO2 variability.

Lee, Eunjee↗
NASA NTRS2019

Statistical Study on Multispacecraft Widespread Solar Energetic Particle Events During Solar Cycle 24

We conduct a statistical study on the large three-spacecraft widespread solar energetic particle (SEP) events. Longitudinal distributions of the peak intensities, onset delays, and relation between the SEP intensity, coronal mass ejection (CME) shock speed, width, and the kinetic energy of the CME have been investigated. We apply a Gaussian fit to obtain the SEP intensity Io and distribution width and a forward-modeling fit to determine the true shock speed and true CME width. We found a good correction between and connection angel to the flare site and Io and the kinetic energy of the CME. By including the true chock speed and true CME widths, we reduce root-mean-square errors on the predicted SEP intensity by ~41% for protons compared to Richards et al.'s (2014, https://doi.org/10.1007/s11207-014-0524-8) prediction. The improved correction between the CME kinetic energy and SEP intensity provides strong evidence for the CME-shock acceleration theory of SEPs. In addition, we found that electron and proton release time delays (DTs) relative to Type II radio bursts increase with connection angles. The average electron (proton) DT is ~14 (32) min for strongly anisotropic events and ~2.5 (4.4) hr for weakly anisotropic events. Poor magnetic connectivity and large scattering effects are two main reasons to cause large delays.

Xie, H.↗
NASA NTRS2004

X Persei: The X-Ray Halo and Spectrum of a High-Latitude X-Ray Binary

The observations were completed on February 25,2003. Although the source was in the FOV for 31.4 ksec, only 18.2 ksec of data were usable due to a strong flare in the first part of the observations. We have extracted the X-ray halo from the good portion of the data, and were then faced with the problem of calibrating the far-off-axis point spread function, which is needed only for X-ray halo analysis; the same problem affected Chandra halo observations. We used data from 3C273, MCG 6-30-15, LMC X-1, and Her X-1 to measure the PSF, and found that it is reasonably well fit with a power law of the form PSF(theta) = A theta^-G, where A = 0.0034 arcmin^-2, and G = 3.05 for energies between 1-4 keV. This suggests there are fewer large dust grains along the When fitting the spectrum of X Persei, we found NH = 3e21 cm^-2, as expected. However, the X-ray halo (using a Mathis, Rumpl, Nordsieck 1977 dust model) required at most a column density of 1.4+/-0.1 e21 cm^-2; other models required sightline to X Per than would have been expected. In addition, a smoothly distributed dust model fit the observations better than a single cloud model, also against our expectations. We are in the process of writing a paper to be submitted to ApJ with these results, and will also present them at the 2004 HEAD meeting in New Orleans.

Mushotzky, Richard↗
DOE OSTI2024

Deep Learning and Likelihood Approaches for Viral Phylogeography Converge on the Same Answers Whether the Inference Model Is Right or Wrong

Abstract Analysis of phylogenetic trees has become an essential tool in epidemiology. Likelihood-based methods fit models to phylogenies to draw inferences about the phylodynamics and history of viral transmission. However, these methods are often computationally expensive, which limits the complexity and realism of phylodynamic models and makes them ill-suited for informing policy decisions in real-time during rapidly developing outbreaks. Likelihood-free methods using deep learning are pushing the boundaries of inference beyond these constraints. In this paper, we extend, compare, and contrast a recently developed deep learning method for likelihood-free inference from trees. We trained multiple deep neural networks using phylogenies from simulated outbreaks that spread among 5 locations and found they achieve close to the same levels of accuracy as Bayesian inference under the true simulation model. We compared robustness to model misspecification of a trained neural network to that of a Bayesian method. We found that both models had comparable performance, converging on similar biases. We also implemented a method of uncertainty quantification called conformalized quantile regression that we demonstrate has similar patterns of sensitivity to model misspecification as Bayesian highest posterior density (HPD) and greatly overlap with HPDs, but have lower precision (more conservative). Finally, we trained and tested a neural network against phylogeographic data from a recent study of the SARS-Cov-2 pandemic in Europe and obtained similar estimates of region-specific epidemiological parameters and the location of the common ancestor in Europe. Along with being as accurate and robust as likelihood-based methods, our trained neural networks are on average over 3 orders of magnitude faster after training. Our results support the notion that neural networks can be trained with simulated data to accurately mimic the good and bad statistical properties of the likelihood functions of generative phylogenetic models.

Evolutionary Biology↗
NASA NTRS1982

Gravitational models for mission planning

A fitted truncated model is developed and any differences between this fitted model and one derived by simply truncating are analyzed. Based on the study, recommendations are made for an appropriate model for use in a mission planning environment.

Mueller, A. C.↗
DOE OSTI2021

Monte Carlo Study of Imaging Plate Response to Laser-Driven Aluminum Ion Beams

We measured the response of BAS-TR imaging plate (IP) to energetic aluminum ions up to 222 MeV, and compared it with predictions from a Monte Carlo simulation code using two different IP response models. Energetic aluminum ions were produced with an intense laser pulse, and the response was evaluated from cross-calibration between CR-39 track detector and IP energy spectrometer. For the first time, we obtained the response function of the BAS-TR IP for aluminum ions with a kinetic energy as high as 222 MeV. On close examination of the two IP response models, we confirm that the exponential model fits our experimental data better. Moreover, we find that the IP sensitivity in the exponential model is nearly constant in this energy range, suggesting that the response function can be determined even with little experimental data.

Won, Junho↗
NASA NTRS1986

Variability studies of 3C 371

The compact extragalactic radio source, 3C 371, was observed with the X-ray detectors of the EXOSAT satellite for 19.5 hours in 2 observing periods separated by 18 days. This resulted in the discovery of X-ray variability of the source on time scales between 8 hours and about 25 minutes and the confirmation of earlier reports of variability on longer time scales. The short time scale variability agrees remarkably well with earlier predictions based on fitting multifrequency data from the source to a relativistically beamed inhomogeneous synchrotron self-Compton (SSC) jet model. This SSC model is frequently applied to BL Lac objects and related sources, such as 3C 371. However, the number of free parameters in model fits tends to be large, and so independent support such as from this work is important. The X-ray spectral results for 3C 371 also may provide qualitative support for the SSC model in that the spectrum probably consists of two components, with the steeper one at low photon energies. In terms of the model, the low-energy spectrum would be dominated by synchrotron emission extending down to radio energies, whereas the higher energy X-rays would be dominated by Compton radiation.

Worrall, D. M.↗
DOE OSTI2025

Implementation of Stochastic Gradient Descent in an Automated Glow Peak Identification Software for Multiple Thermoluminescent Dosimeter Types

A glow-curve analysis code was previously developed in C++ to analyze thermoluminescent dosimeter glow curves using automated peak detection while applying a first-order kinetics model. A newer version of this code was implemented to improve the automated peak detection and curve fitting models. The Stochastic Gradient Descent Algorithm was introduced to replace the prior approach of taking first and second-order derivatives for peak detection. Additionally, early stopping mechanisms were invoked to improve the previously used Levenberg-Marquardt Algorithm employed for curve fitting. The two software versions were compared through glow curve analysis of different thermoluminescent dosimeter materials and calculation of the corresponding figures of merit. Altogether improvements were shown, namely an increase in the number of peaks detected and a reduction of the mean figure of merit by approximately 46%.

137Cs↗