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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 NTRS1992

Properties of hybrid CVD/PAN graphite fibers and their bromine intercalation compounds

A hybrid fiber with a PAN core surrounded by a vapor grown carbon fiber (VGCF) sheath was fabricated using a proprietary process. The density, ultimate tensile strength, Young's modulus, and resistivity of pristine and bromine intercalated fibers made by this technique having diameters varying from 5 to 50 microns were compared with the values predicted from the rule of mixtures model. For both the pristine and intercalated fibers, the density, ultimate tensile strength, and Young's modulus of the fibers were lower than predicted, but the resistivity was measured to be consistent with predictions. The lower than theoretical mechanical properties may be evidence of a low density disordered interface between the core and the sheath which would lower the density and degrade the mechanical properties, but would leave the resistivity nearly unaffected. Intercalation had little if any effect on the ultimate tensile strength and Young's modulus, but raised the density by about 11 pct., and lowered the resistivity by an order of magnitude. The diameter dependence of the resistivity showed evidence of a depletion layer of the type found in VGCF.

Gaier, James R.↗
NASA NTRS1992

Chemistry and kinetics of I2 loss in urine distillate and humidity condensate

Time-resolved molecular absorption spectrophotometry of iodinated ersatz humidity condensates and iodinated ersatz urine distillates across the UV and visible spectral regions are used to investigate the chemistry and kinetics of I2 loss in urine distillate and humidity condensate. Single contaminant systems at equivalent concentrations are also employed to study rates of iodine. Pseudo-first order rate constants are identified for ersatz contaminant model mixtures and for individual reactive constituents. The second order bimolecular reaction of elemental iodine with formic acid, producing carbon dioxide and iodine anion, is identified as the primary mechanism underlying the decay of residual I2 in ersatz humidity concentrate.

Atwater, James E.↗
NASA NTRS1992

Extraction of soil and vegetation parameters from high resolution bi-directional reflectance spectra

High spectral resolution reflectance spectra were collected over a semi-desert grassland at both dry and wet season periods. Spectral reflectance measurements were made from several viewing angles at both low and high solar zenith angles. A mixture model was used to separate and extract green vegetation from dry/dead vegetation and soil. The extracted vegetation signal varied greatly with view and sun angle variations such that off-nadir viewing and illuminating angles resulted in the highest vegetation loadings. These variations were normalized with cosine functions for both sun and view angle. These results offer a methodology for standardizing multi-temporal and multi-angular satellite measurements of vegetation activity.

Huete, A. R.↗
NASA NTRS1994

Detecting bimodality in astronomical datasets

We discuss statistical techniques for detecting and quantifying bimodality in astronomical datasets. We concentrate on the KMM algorithm, which estimates the statistical significance of bimodality in such datasets and objectively partitions data into subpopulations. By simulating bimodal distributions with a range of properties we investigate the sensitivity of KMM to datasets with varying characteristics. Our results facilitate the planning of optimal observing strategies for systems where bimodality is suspected. Mixture-modeling algorithms similar to the KMM algorithm have been used in previous studies to partition the stellar population of the Milky Way into subsystems. We illustrate the broad applicability of KMM by analyzing published data on globular cluster metallicity distributions, velocity distributions of galaxies in clusters, and burst durations of gamma-ray sources. FORTRAN code for the KMM algorithm and directions for its use are available from the authors upon request.

Ashman, Keith A.↗
NASA NTRS1995

Constraints on the formation history of the elliptical galaxy NGC 3923 from the colors of its globular clusters

We present a study of the colors of globular clusters associated with the elliptical galaxy NGC 3923. Our final sample consists of Wasington system C and T(sub 1) photometry for 143 globular cluster candidates with an expected contamination of no more than 10%. We find that the color distribution of the NGC 3923 globular cluster system (GCS) is broad and appears to have at least two peaks. A mixture modeling analysis of the color distribution indicates that a two-component model is favored over a single-component one at a high level of confidence (greater than 99%). This evidence for more than one population in the GCS of NGC 3923 is similar to that previously noted for the four other elliptical galaxies for which similar data have been published. Furthermore, we find that the NGC 3923 GCS is redder than the GCSs of previously studed elliptical galaxies of similar luminosity. The median metallicity inferred from our (C-(T(sub 1)))(sub 0) colors is (Fe/H)(sub med) = -0.56, with an uncertainty of 0.14 dex arising from all sources of uncertainty in the mean color. This is more metal rich than the median metallicity found for the GCS of M87 using the same method, (Fe/H)(sub med) = -0.94. Since M87 is more luminous than NGC 3923, this result points to significant scatter about any trend of higher GCS metallicity with increasing galaxy luminosity. We also show that there is a color gradient in the NGC 3923 GCS corresponding to about -0.5 dex in Delta(Fe/H)/Delta(log r). We conclude that the shape of the color distribution of individual GCSs and the variation in mean color among the GCSs of ellipticals are difficult to understand if elliptical galaxies are formed in a single protogalactic collapse. Models in which ellipticals and their globular clusters are formed in more than one event, such as a merger scenario, are more successful in accounting for these observations.

Zepf, Stephen E.↗
NASA NTRS1999

New Materials for EMI Shielding

Graphite fibers intercalated with bromine or similar mixed halogen compounds have substantially lower resistivity than their pristine counterparts, and thus should exhibit higher shielding effectiveness against electromagnetic interference. The mechanical and thermal properties are nearly unaffected, and the shielding of high energy x-rays and gamma rays is substantially increased. Characterization of the resistivity of the composite materials is subtle, but it is clear that the composite resistivity is substantially lowered. Shielding effectiveness calculations utilizing a simple rule of mixtures model yields results that are consistent with available data on these materials.

Gaier, James R.↗
NASA NTRS1990

Exploring the Relationship Between Reflectance Red Edge and Chlorophyll Content in Slash Pine

Chlorophyll is a key indicator of the physiological status of a forest canopy. However, its distribution may vary greatly in time and space, so that the estimation of chlorophyll content of canopies or branches by extrapolation from leaf values obtained by destructive sampling is labor intensive and potentially inaccurate. Chlorophy11 content is related positively to the point of maximum slope in vegetation reflectance spectra which occurs at wavelengths between 690-740 nm and is known as the "red edge." The red edge of needles on individual slash pine (Piniis elliottii Engelm.) branches and in whole forest canopies was measured with a spectroradiometer. Branches were measured on the ground against a spectrally flat reflectance target and canopies were measured from observation towers against a spectrally variable understory and forest floor. There was a linear relationship between red edge and chlorophyll content of branches (R(exp 2) = 0.91). Measurements of the red edge and this relationship were used to estimate the chlorophyll content of other branches with an error that was lower than that associated with the colorimetric (laboratory) method. There was no relationship between the red edge and the chlorophyll content of whole canopies. This can be explained by the overriding influence of the understory and forest floor, an influence that was illustrated by spectral mixture modeling. The results suggest that the red edge could be used to estimate the chlorophyll content in branches but it is unlikely to be of value for the estimation of chlorophyll content in canopies unless the canopy cover is high.

Curran, Paul J.↗
NASA NTRS1994

AutoClass: A Bayesian Approach to Classification

We describe a Bayesian approach to the untutored discovery of classes in a set of cases, sometimes called finite mixture separation or clustering. The main difference between clustering and our approach is that we search for the "best" set of class descriptions rather than grouping the cases themselves. We describe our classes in terms of a probability distribution or density function, and the locally maximal posterior probability valued function parameters. We rate our classifications with an approximate joint probability of the data and functional form, marginalizing over the parameters. Approximation is necessitated by the computational complexity of the joint probability. Thus, we marginalize w.r.t. local maxima in the parameter space. We discuss the rationale behind our approach to classification. We give the mathematical development for the basic mixture model and describe the approximations needed for computational tractability. We instantiate the basic model with the discrete Dirichlet distribution and multivariant Gaussian density likelihoods. Then we show some results for both constructed and actual data.

Stutz, John↗
NASA NTRS2010

Initialization and Restart in Stochastic Local Search: Computing a Most Probable Explanation in Bayesian Networks

For hard computational problems, stochastic local search has proven to be a competitive approach to finding optimal or approximately optimal problem solutions. Two key research questions for stochastic local search algorithms are: Which algorithms are effective for initialization? When should the search process be restarted? In the present work we investigate these research questions in the context of approximate computation of most probable explanations (MPEs) in Bayesian networks (BNs). We introduce a novel approach, based on the Viterbi algorithm, to explanation initialization in BNs. While the Viterbi algorithm works on sequences and trees, our approach works on BNs with arbitrary topologies. We also give a novel formalization of stochastic local search, with focus on initialization and restart, using probability theory and mixture models. Experimentally, we apply our methods to the problem of MPE computation, using a stochastic local search algorithm known as Stochastic Greedy Search. By carefully optimizing both initialization and restart, we reduce the MPE search time for application BNs by several orders of magnitude compared to using uniform at random initialization without restart. On several BNs from applications, the performance of Stochastic Greedy Search is competitive with clique tree clustering, a state-of-the-art exact algorithm used for MPE computation in BNs.

Mengshoel, Ole J.↗
NASA NTRS2012

Simulation of Complex Cracking in Plain Weave C/SiC Composite under Biaxial Loading

Finite element analysis is performed on a mesh, based on computed geometry of a plain weave C/SiC composite with assumed internal stacking, to reveal the pattern of internal damage due to biaxial normal cyclic loading. The simulation encompasses intertow matrix cracking, matrix cracking inside the tows, and separation at the tow-intertow matrix and tow-tow interfaces. All these dissipative behaviors are represented by traction-separation cohesive laws. Not aimed at quantitatively predicting the overall stress-strain relation, the simulation, however, does not take the actual process of fiber debonding into account. The fiber tows are represented by a simple rule-of-mixture model where the reinforcing phase is a hypothetical one-dimensional material. Numerical results indicate that for the plain weave C/SiC composite, 1) matrix-crack initiation sites are primarily determined by large intertow matrix voids and interlayer tow-tow contacts, 2) the pattern of internal damage strongly depends on the loading path and initial stress, 3) compressive loading inflicts virtually no damage evolution. KEY WORDS: ceramic matrix composite, plain weave, cohesive model, brittle failure, smeared crack model, progressive damage, meso-mechanical analysis, finite element.

Cheng, Ron-Bin↗
NASA NTRS2021

Highland Lakes Water Resources: Using NASA Earth Observations to Improve Detection Systems for Harmful Algal Events in the Highland Lakes in Central Texas

Beginning in 2019, harmful algal events in Austin, Texas, caused canine deaths in the Lady Bird Lake and Lake Travis reservoirs. These reservoirs are part of the larger Highland Lakes chain, managed by the Lower Colorado River Authority (LCRA) and the City of Austin Department of Watershed Protection (CoA DWP), which fulfill municipal, commercial, and agricultural water demands. Given the recent increase in favorable algal event conditions in central Texas, the LCRA and CoA DWP partnered with NASA DEVELOP to improve algal event early-warning systems through the application of remote sensing and machine learning. An Earth observation-based algal monitoring system will assist the responsible agencies in predicting algal conditions and communicating hazards to the public. The NASA DEVELOP team utilized Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) data to produce products including chlorophyll-a concentrations, cyanobacteria detections, turbidity, and water surface temperature. Chlorophyll-a concentrations were retrieved with a pre-trained machine learning model (mixture density network) and spectral indices, while the other products were derived from spectral indices. In situ field data were used to validate and quantify uncertainties for each product. The validations show strong correlations for chlorophyll-a and water surface temperature. Time series analyses of chlorophyll-a concentrations show peaks in the severe drought years (2015 and 2016). This project's resulting products enable monitoring of environmental proxies relevant to algal event presence in the Highland Lakes chain and will ultimately support water management, decision making, and risk communication.

Kaitlynn Hietpas↗
NASA NTRSDate not supplied

Three-Dimensional CFD Simulations in the Film Boiling Regime During Liquid Nitrogen Chilldown Process

The paper presents a three-dimensional CFD simulation for predicting the liquid nitrogen chilldown process through a SS-304 tube. A dispersed mixture model has been employed to predict the two-phase flow behavior whereas the Lee phase change model has been used to predict the boiling process. Cryogenic chilldown process starts with a pure vapor region and proceeds to film, transition and nucleate boiling regimes before reaching the single phase liquid flow and complete chilldown of the transfer line. The present numerical model predicted the film boiling regime accurately and the slope of the chilldown curve deviates slightly from the experimental curve towards the transition and nucleate boiling regime, which needs further investigation. The contours of liquid volume fraction and temperature profiles has been presented to understand the inverted annular film flows in the film boiling regime in detail. In future, the present CFD model will be extended to a wide range of chilldown conditions and different cryogenic fluids to develop a robust methodology.

CFD↗
NASA NTRSDate not supplied

Unsupervised Change Detection for Space Habitats Using 3D Point Clouds

This work presents an algorithm for scene change detection from point clouds to enable autonomous robotic caretaking in future space habitats. Autonomous robotic systems will help maintain future deep-space habitats, such as the Gateway space station, which will be uncrewed for extended periods. Existing scene analysis software used on the International Space Station (ISS) relies on manually-labeled images for detecting changes. In contrast, the algorithm presented in this work uses raw, unlabeled point clouds as inputs. The algorithm first applies modified Expectation-Maximization Gaussian Mixture Model (GMM) clustering to two input point clouds. It then performs change detection by comparing the GMMs using the Earth Mover’s Distance. The algorithm is validated quantitatively and qualitatively using a test dataset collected by an Astrobee robot in the NASA Ames Granite Lab comprising single frame depth images taken directly by Astrobee and full-scene reconstructed maps built with RGB-D and pose data from Astrobee. The runtimes of the approach are also analyzed in depth. The source code is publicly released to promote further development.

robotics↗
DOE OSTI2026

The Kinematically Hot, Extremely Metal-Poor C-19 Stellar Stream in DESI DR2

Stellar streams are the result of a host galaxy's gravitational potential tidally disrupting satellite dwarf galaxies and globular clusters (GCs), causing them to grow leading and trailing tidal tails. The C-19 stellar stream is an extremely metal-poor stellar population, showing chemical abundance patterns characteristic of a globular cluster. However, its large velocity dispersion is difficult to reconcile with a conventional, purely baryonic, disrupting-GC progenitor. Current techniques for stream characterization are primarily applied to Gaia DR3, relying heavily on proper motion measurements. Using the Dark Energy Spectroscopic Instrument (DESI), which provides radial velocities and metallicites for over 10 million stars reaching significantly fainter magnitudes than comparable surveys, we employ a mixture model approach to jointly characterize stream populations in proper motions, radial velocities, and metallicities against a Milky Way halo background. By applying this framework to the C-19 stellar stream, we identify a total of 47 spectroscopically confirmed member stars, of which 41 are newly identified and only 6 were previously reported in the literature. In this work, we measure a velocity dispersion of $7.8^{+1.5}_{-1.3}$ km s$^{-1}$ and a mean metallicity of [Fe/H] = $-3.36^{+0.12}_{-0.10}$. We further identify a novel 'spur' feature within the stream. We conclude that our measurements are in line with previous works identifying C-19 as a 'hot', metal-poor stream. In forthcoming work, we will apply this approach to many more streams in the DESI footprint, enabling population-level comparisons with predictions from simulations.

Mohammed, Nasser [Toronto U.] (ORCID:0009000812240↗
DOE OSTI2026

The Kinematically Hot, Extremely Metal-Poor C-19 Stellar Stream in DESI DR2

Stellar streams are the result of a host galaxy's gravitational potential tidally disrupting satellite dwarf galaxies and globular clusters (GCs), causing them to grow leading and trailing tidal tails. The C-19 stellar stream is an extremely metal-poor stellar population, showing chemical abundance patterns characteristic of a globular cluster. However, its large velocity dispersion is difficult to reconcile with a conventional, purely baryonic, disrupting-GC progenitor. Current techniques for stream characterization are primarily applied to Gaia DR3, relying heavily on proper motion measurements. Using the Dark Energy Spectroscopic Instrument (DESI), which provides radial velocities and metallicites for over 10 million stars reaching significantly fainter magnitudes than comparable surveys, we employ a mixture model approach to jointly characterize stream populations in proper motions, radial velocities, and metallicities against a Milky Way halo background. By applying this framework to the C-19 stellar stream, we identify a total of 47 spectroscopically confirmed member stars, of which 41 are newly identified and only 6 were previously reported in the literature. In this work, we measure a velocity dispersion of $7.8^{+1.5}_{-1.3}$ km s$^{-1}$ and a mean metallicity of [Fe/H] = $-3.36^{+0.12}_{-0.10}$. We further identify a novel 'spur' feature within the stream. We conclude that our measurements are in line with previous works identifying C-19 as a 'hot', metal-poor stream. In forthcoming work, we will apply this approach to many more streams in the DESI footprint, enabling population-level comparisons with predictions from simulations.

Mohammed, Nasser [Toronto U.] (ORCID:0009000812240↗
DOE OSTI2026

Characterizing the GD-1 Stream with DESI DR2 Data: Thin Stream and Hot Cocoon

GD-1 is among the longest, coldest stellar streams in the Milky Way, making it an ideal target for probing dark matter substructure through dynamical heating. We present a catalog of 608 spectroscopically confirmed GD-1 members from the first three years of Dark Energy Spectroscopic Instrument (DESI) observations. This constitutes the largest homogeneous spectroscopic sample of GD-1, doubling the number of members previously available only through heterogeneous compilations combining multiple surveys with different systematics. Using these data, we derive updated stream tracks in sky position, proper motion, and radial velocity that extend over $100^\circ$ of the stream. We apply a Gaussian mixture model to decompose the stream into a dynamically cold thin component ($σ_V = 2.49\pm 0.28$ km s$^{-1}$, width $= 0.23\pm0.01^\circ$) and a kinematically hot cocoon ($σ_V = 6.13\pm0.75$ km s$^{-1}$, width $= 2.18\pm0.17^\circ$). The cocoon contains $\sim30\%$ of members and its velocity dispersion is consistent with $\sim11$ Gyr of heating by cold dark matter subhalos. We also detect a large proper motion dispersion ($41.36\pm4.98$ km s$^{-1}$) along the stream direction in the cocoon component. This feature indicates a significant line-of-sight distance spread in the cocoon, and its origin will be further explored in a forthcoming paper. These measurements demonstrate the power of DESI spectroscopy for characterizing the multi-component phase-space structure of stellar streams and constraining small-scale dark matter substructure.

Jarvis, Emma [Toronto U.] (ORCID:0009000656127336)↗
DOE OSTI2025

BatteryPro: A Python Toolkit for Battery Data Analysis and Machine Learning Predictions

Analyzing battery test data for research & development can be time-consuming since battery tests often run on the order of months to years, generating large volumes of data. BatteryPro is a comprehensive Python package and software designed to facilitate advanced analysis and performance predictions for battery test data. Developed for battery researchers, it supports data types from widely used battery testing instruments, including MACCOR and Biologic cycling systems. The software provides a variety of tools for extracting and plotting key battery parameters such as time, voltage, capacity, current, and pressure. In addition to its extensive data analysis capabilities, BatteryPro features a dedicated machine learning module that employs a Bayesian Gaussian Mixture Model (GMM) to predict battery performance and degradation. Users can generate synthetic capacity fade data, calculate fade metrics, and leverage predictive models to forecast long-term battery behavior. The software's graphical user interface (GUI) enhances usability, allowing researchers to upload, merge, and analyze multiple data files with full customizability. The GUI also supports machine learning predictions, enabling users to fit models and make predictions based on selected data and parameters. BatteryPro is built using QtDesigner, scikit-learn, matplotlib, and pandas, ensuring a high level of customization, flexibility, and accuracy in battery data analysis. This tool aims to empower researchers with the ability to perform detailed battery analysis and make informed predictions, ultimately advancing the field of battery research.

25 - ENERGY STORAGE↗
NASA NTRS1992

Effects of grain size and shape in modeling reflectance spectra of mineral mixtures

An 'isograin model' of reflectance from particulate material is presented. The model treats reflectance as a series of grain interactions, incorporating optical constants of each material and two parameters that model the activity of scattering and absorption in the vertical and the horizontal directions, respectively. Mixing ratios of components in mixture are treated as variables and are calculated using least-squares fit. Grain size and shape have a significant effect on the accuracy of predicting mixing ratios but can be modeled by introducing the concept of effective grain size. The model calculates spectra in good agreement with measured spectra and models reflectance within 1 percent and mixing ratios within 5 wt pct.

Hiroi, T.↗