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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

An Archival Chandra and XMM-Newton Survey of Type 2 Quasars

In order to investigate obscuration in high-luminosity type 2 active galactic nuclei (AGNs), we analyzed Chandra and XMM-Newton archival observations for 71 type 2 quasars detected at 0.05 < z < 0.73, which were selected based on their [O III] lambda5007 emission lines. For 54 objects with good spectral fits, the observed hard X-ray luminosity ranges from 2 × 10(exp 41) to 5.3 × 10(exp 44) erg s(exp −1), with a median of 1.1 × 10(exp 43) erg s(exp −1). We find that the means of the column density and photon index of our sample are log N(sub H) = 22.9 cm(exp −2) and gamma = 1.87, respectively. From simulations using a more physically realistic model, we find that the absorbing column density estimates based on simple power-law models significantly underestimate the actual absorption in approximately half of the sources. Eleven sources show a prominent Fe K alpha emission line (EW>100 eV in the rest frame) and we detect this line in the other sources through a joint fit (spectral stacking). The correlation between the Fe K alpha and [O III] fluxes and the inverse correlation of the equivalent width of the Fe Kα line with the ratio of hard X-ray and [O III] fluxes is consistent with previous results for lower luminosity Seyfert 2 galaxies. We conclude that obscuration is the cause of the weak hard X-ray emission rather than intrinsically low X-ray luminosities. We find that about half of the population of optically selected type 2 quasars are likely to be Compton thick. We also find no evidence that the amount of X-ray obscuration depends on the AGN luminosity (over a range of more than three orders of magnitude in luminosity).

quasars: general↗
NASA NTRS2015

Computational and Experimental Unsteady Pressures for Alternate SLS Booster Nose Shapes

Delayed Detached Eddy Simulation (DDES) predictions of the unsteady transonic flow about a Space Launch System (SLS) configuration were made with the Fully UNstructured Three-Dimensional (FUN3D) flow solver. The computational predictions were validated against results from a 2.5% model tested in the NASA Ames 11-Foot Transonic Unitary Plan Facility. The peak C(sub p,rms) value was under-predicted for the baseline, Mach 0.9 case, but the general trends of high C(sub p,rms) levels behind the forward attach hardware, reducing as one moves away both streamwise and circumferentially, were captured. Frequency of the peak power in power spectral density estimates was consistently under-predicted. Five alternate booster nose shapes were assessed, and several were shown to reduce the surface pressure fluctuations, both as predicted by the computations and verified by the wind tunnel results.

Braukmann, Gregory J.↗
NASA NTRS2015

A High Performance Computing Approach to Tree Cover Delineation in 1-m NAIP Imagery Using a Probabilistic Learning Framework

Tree cover delineation is a useful instrument in deriving Above Ground Biomass (AGB) density estimates from Very High Resolution (VHR) airborne imagery data. Numerous algorithms have been designed to address this problem, but most of them do not scale to these datasets, which are of the order of terabytes. In this paper, we present a semi-automated probabilistic framework for the segmentation and classification of 1-m National Agriculture Imagery Program (NAIP) for tree-cover delineation for the whole of Continental United States, using a High Performance Computing Architecture. Classification is performed using a multi-layer Feedforward Backpropagation Neural Network and segmentation is performed using a Statistical Region Merging algorithm. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on Conditional Random Field, which helps in capturing the higher order contextual dependencies between neighboring pixels. Once the final probability maps are generated, the framework is updated and re-trained by relabeling misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates. The tree cover maps were generated for the whole state of California, spanning a total of 11,095 NAIP tiles covering a total geographical area of 163,696 sq. miles. The framework produced true positive rates of around 88% for fragmented forests and 74% for urban tree cover areas, with false positive rates lower than 2% for both landscapes. Comparative studies with the National Land Cover Data (NLCD) algorithm and the LiDAR canopy height model (CHM) showed the effectiveness of our framework for generating accurate high-resolution tree-cover maps.

Segments↗
NASA NTRS2017

Evidence of Significant Energy Input in the Late Phase of A Solar Flare from NuSTAR X-Ray Observations

We present observations of the occulted active region AR 12222 during the third Nuclear Spectroscopic Telescope ARray (NuSTAR) solar campaign on 2014 December 11, with concurrent Solar Dynamics Observatory (SDO)/ AIA and FOXSI-2 sounding rocket observations. The active region produced a medium-size solar flare 1 day before the observations, at approximately 18 UT on 2014 December 10, with the post-flare loops still visible at the time of NuSTAR observations. The time evolution of the source emission in the SDO/AIA 335 Å channel reveals the characteristics of an extreme-ultraviolet late-phase event, caused by the continuous formation of new post-flare loops that arch higher and higher in the solar corona. The spectral fitting of NuSTAR observations yields an isothermal source, with temperature 3.8-4.6 MK, emission measure (0.3-1.8) × 1046 cm−3, and density estimated at (2.5-6.0) × 108 cm−3. The observed AIA fluxes are consistent with the derived NuSTAR temperature range, favoring temperature values in the range of 4.0-4.3 MK. By examining the post-flare loops' cooling times and energy content, we estimate that at least 12 sets of post-flare loops were formed and subsequently cooled between the onset of the flare and NuSTAR observations, with their total thermal energy content an order of magnitude larger than the energy content at flare peak time. This indicates that the standard approach of using only the flare peak time to derive the total thermal energy content of a flare can lead to a large underestimation of its value.

gamma rays↗
NASA NTRS2017

Small Orbital Stereo Tracking Camera Technology Development

Any exploration vehicle assembled or Spacecraft placed in LEO or GTO must pass through this debris cloud and survive. Large cross section, low thrust vehicles will spend more time spiraling out through the cloud and will suffer more impacts.Better knowledge of small debris will improve survival odds. Current estimated Density of debris at various orbital attitudes with notation of recent collisions and resulting spikes. Orbital Debris Tracking and Characterization has now been added to NASA Office of Chief Technologists Technology Development Roadmap in Technology Area 5 (TA5.7)[Orbital Debris Tracking and Characterization] and is a technical gap in the current National Space Situational Awareness necessary to safeguard orbital assets and crews due to the risk of Orbital Debris damage to ISS Exploration vehicles. The Problem: Traditional orbital trackers looking for small, dim orbital derelicts and debris typically will stare at the stars and let any reflected light off the debris integrate in the imager for seconds, thus creating a streak across the image. The Solution: The Small Tracker will see Stars and other celestial objects rise through its Field of View (FOV) at the rotational rate of its orbit, but the glint off of orbital objects will move through the FOV at different rates and directions. Debris on a head-on collision course (or close) will stay in the FOV at 14 Km per sec. The Small Tracker can track at 60 frames per sec allowing up to 30 fixes before a near-miss pass. A Stereo pair of Small Trackers can provide range data within 5-7 Km for better orbit measurements.

Bryan, Tom↗
NASA NTRS2018

Infrared Band Strengths for Amorphous and Crystalline Methyl Propionate, a Candidate Interstellar Molecule

Mid-infrared spectra of amorphous and crystalline methyl propionate, CH3CH2COOCH3, are presented for the first time from a single laboratory, along with measurements of the refractive index of each solid form. Density estimates are made and IR band strengths and absorption coefficients are calculated. Vapor pressures of crystalline methyl propionate at 140-150 K are reported along with an enthalpy of sublimation. Spectroscopic results are compared to a recent study of this compound, and the phase of methyl propionate in that work is identified. Several applications are described.

Astrochemistry↗
NASA NTRS2019

Architecture and Dynamics of Kepler's Multi-Transiting Planet Systems.III. Comprehensive Investigation Using All Four Years of Kepler Mission Data

We study the orbital architectures of planetary systems orbiting within ∼1 AU of their stars by analyzing the ensemble of Kepler systems having two or more planet candidates. We use data from the entire Kepler mission, and in many cases we apply improved analysis techniques (e.g., replacing histograms by top-hat Kernel Density Estimators that avoid the loss of information resulting from choosing a particular phase for the bin boundaries) to extend and enhance the studies of Lissauer et al. (2011, ApJS 197, 8) and Fabrycky et al. (2014, ApJ 790, 146).These data show ~ 1700 transiting planet candidates in > 600 multiple-planet systems, far more than were available for our previous two studies. The increased numbers and better information about planetary radii and the properties of stellar hosts made possible by Gaia DR2 allow more statistically-robust analyses of the entire ensemble of Kepler multis as well as independent analyses of subsets of the population. We are thus able to contrast the dynamical configurations of small and large planets, short-period and longer-period planets, and planets orbiting various types of host stars. We reinforce our previous findings that most pairs of planets within the same system are neither in nor near low-order mean motion resonances and that there is a substantial excess of planets having period ratios slightly larger than those of first-order mean-motion resonances. However, neglecting three systems whose planets are locked in 3- body resonances and summing over all first-order mean motion resonances, the deficit of planet pairs with period ratios just narrow of resonance is as large as the excess of planets wide of resonance (within statistical uncertainties), suggesting that overall there is no overall excess of planet pairs in the vicinity of resonance. Other aspects of our study, including estimates of the typical relative inclinations of planetary orbits and their variations as functions of orbital period, planet sizes and stellar properties, are in progress, with results expected to be available for presentation by the time of the conference.

Lissauer, Jack↗
NASA NTRS2020

Microscale Characterization of Damage Accumulation in CMCs

The developing roles of damage mechanisms in the failure response of SiC/SiC minicomposites was investigated by the characterization of microscale damage accumulation with respect to microstructure. A multi-modal approach combining spatially resolved acoustic emission (AE) with tensile testing in-SEM (scanning electron microscope) was used to simultaneously examine surface (observed in-SEM) and bulk damage (monitored via AE). Strong agreement was shown between the evolving crack density estimated by AE and in-SEM measurements. The following were observed: (i) in-plane matrix content and distribution impacted crack growth; (ii) spatially-distributed matrix cracks generated varying stress-dependent AE; and (iii) certain individual cracks became more probable failure locations due to unique combinations of damage mechanisms that drove their growth. This approach enabled characterizing potential failure determinants and suggests that early damage behavior is related to certain microstructural features (e.g. surface flaws), while subsequent damage behavior is coupled to interactions of local mechanisms evolving with stress.

ceramic matrix composites↗
NASA NTRS2021

Application of Gaussian Mixture Regression for the Correction of Low Cost PM2.5 Monitoring Data in Accra, Ghana

Low-cost sensors (LCSs) for air quality monitoring have enormous potential to improve air quality data coverage in resource-limited parts of the world such as sub-Saharan Africa. LCSs, however, are affected by environment and source conditions. To establish high-quality data, LCSs must be collocated and calibrated with reference grade PM2.5 monitors. From March 2020, a low-cost PurpleAir PM2.5 monitor was collocated with a Met One Beta Attenuation Monitor 1020 in Accra, Ghana. While previous studies have shown that multiple linear regression (MLR) and random forest regression (RF) can improve accuracy and correlation between PurpleAir and reference data, MLR and RF yielded suboptimal improvement in the Accra collocation (R2 = 0.81 and R2 = 0.81, respectively). We present the first application of Gaussian mixture regression (GMR) to air quality data calibration and demonstrate improvement over traditional methods by increasing the collocated PM2.5 correlation and accuracy to R2 = 0.88 and MAE = 2.2 μg/cu. m. Gaussian mixture models (GMMs) are a probability density estimator and clustering method from which nonlinear regressions that tolerate missing inputs can be derived. We find that even when given missing inputs, GMR provides better correlation than MLR and RF performed with complete data. GMR also allows us to estimate calibration certainty. When evaluated, 95% confidence intervals agreed with reference PM2.5 data 96% of the time, suggesting that the model accurately assesses its own confidence. Additionally, clustering within the GMM is consistent with climate characteristics, providing confidence that the calibration approach can learn underlying relationships in data.

Sensors↗
NASA NTRS2022

The Origins of Long-Term Variability in Martian Upper Atmospheric Densities

We quantify and interpret the long-term variability of dayside Martian upper thermosphere and lower exosphere densities within 180–275 km altitudes. Atmospheric CO2, N2, O, and Ar densities are from NASA Mars Atmosphere and Volatile EvolutioN (MAVEN) observations during the time period of 2015–2020 near solar minimum. These neutral measurements, together with contemporaneous solar irradiance measurements at Mars, enable disentanglement of the orbital effect (due to the annual Sun-Mars distance change with solar longitude) and the solar extreme ultraviolet (EUV) effect in atmospheric density variations. The relative importance of these two effects, which is obtained using a statistical method of Dominance Analysis, reveals the competition between the indirect effect of solar infrared (via the upward coupling from the middle atmosphere) and the direct effect of solar EUV (due to local heating). Our results show that, unlike the orbital effect which is relatively constant at low altitudes and then decreases with increasing altitude, the solar EUV effect nearly monotonically increases. These two effects are comparable at high altitudes (about 240/270/205 km for CO2/N2/O). This analysis is extended to include long-term exospheric mass density estimates near 400 km from Mars Global Surveyor and Mars Odyssey data, with a focus on representative solar cycle phases of solar minimum and maximum. It is found that near 400 km, the orbital effect is always a key driver regardless of the solar cycle phase, while the solar EUV effect plays a minor role during solar minimum and is greatly enhanced and slightly exceeds the orbital effect during solar maximum.

Xiaohua Fang↗
NASA NTRSDate not supplied

New Solid Propellant Additives: Evaluation for NASA Mission Use Cases

Researchers and small companies have been investigating and developing new solid rocket motor propellant ingredients with renewed vigor. Lithium-aluminum alloy is an ingredient of particular interest, but most of the advertisement about its benefits are focused on defense tactical rocket applications. This paper develops a framework to evaluate this and other propellants against standard aluminized composite propellant for mass-limited missions like many NASA in-space applications. It shows a way to estimate density’s effect with an exponent less than 1 depending on mission and technology parameters, and an algorithm for directly calculating delta-velocity or payload performance improvement.

Lithium-aluminum↗
DOE OSTI2013

Analysis of Variability and Uncertainty in Wind Power Forecasting: An International Comparison: Preprint

One of the critical challenges of wind power integration is the variable and uncertain nature of the resource. This paper investigates the variability and uncertainty in wind forecasting for multiple power systems in six countries. An extensive comparison of wind forecasting is performed among the six power systems by analyzing the following scenarios: (i) wind forecast errors throughout a year; (ii) forecast errors at a specific time of day throughout a year; (iii) forecast errors at peak and off-peak hours of a day; (iv) forecast errors in different seasons; (v) extreme forecasts with large overforecast or underforecast errors; and (vi) forecast errors when wind power generation is at different percentages of the total wind capacity. The kernel density estimation method is adopted to characterize the distribution of forecast errors. The results show that the level of uncertainty and the forecast error distribution vary among different power systems and scenarios. In addition, for most power systems, (i) there is a tendency to underforecast in winter; and (ii) the forecasts in winter generally have more uncertainty than the forecasts in summer.

17 WIND ENERGY↗
DOE OSTI2016

Muons and seismic: a dynamic duo for the shallow subsurface?

Measurements of muon flux and direction at depth provides constraints on density distribution, both spatially and as a function of time. Combination of muon measurements and seismic data provide the potential for improved density estimation and the resolution of elastic parameters.

Inversion↗
DOE OSTI2015

Spatial Ecology of Blanding’s Turtles (Emydoidea blandingii) in Southcentral New Hampshire with Implications to Road Mortality

Understanding the spatial ecology and habitat requirements of rare turtle species and the factors that threaten their populations is important for the success of long-term conservation programs. We present results on an eight-year field study in which we used radiotelemetry to monitor the activity and habitat use of 23 adult (male, n = 7; female, n = 16) Blanding’s turtles in southcentral New Hampshire. We found that females occupied home ranges (as defined by minimum convex polygons) that were approximately two times larger than the home ranges of males. Despite the sex difference in home range size, we found no sex difference in core area size (defined as the 50% kernel density estimate). We found that activity patterns varied by season, with increased activity each month after hibernation, and peak activity coinciding with the late spring-early summer nesting season. We observed sex-based and seasonal differences in wetland use. Males appeared to prefer emergent and scrub-shrub wetlands in each season, whereas females preferred scrub-shrub wetlands in spring and ponds in summer and fall. We identified road mortality risk as a potentially important threat for this population because females crossed roads ten times more frequently than males (based on proportion of observations). The preservation of wetland networks, as well as the implementation of measures to minimize road mortality, are important considerations for the long-term persistence of this population.

Activity↗
DOE OSTI2020

Real-time Intrusion Detection for High-bandwidth Research Networks using Unsupervised Deep Learning

Nowadays, data generated by large-scale scientific experiments are on the scale of petabytes per month. These data are transferred through dedicated high-bandwidth networks across distributed collaboration sites for processing, storage, and analysis. Like general purpose networks, research networks experience attacks. However, monitoring traffic in such high-bandwidth network environments is challenging with conventional deep packet inspection (DPI)- based network intrusion detection systems (NIDS). Recently, machine learning (ML) algorithms have shown potential in detecting anomalous patterns with better generalizability and faster speed. Compared to the general purpose networks, traffic movement in research network is less volatile and more likely to benefit from the ML-based anomaly analysis. We present ThunderSecure, an efficient AI-powered system for detecting anomalies in 100G research networks. ThunderSecure implements a high-throughput packet processing infrastructure using multi-cores and GPUs. It takes time-series statistics from a variety of network measurements and uses 2D Convolutional Neural Network (CNN) to explore both spatial and temporal dependencies in network data stream. Patterns of science data traffic are learned with a one-class neural network, which blends an Adversarial Autoencoder (AAE) with Gaussian mixture density estimation. Testing traffic flows exhibiting significant deviations from the learned baseline of normality are marked as anomalies. We trained ThunderSecure on hundreds of billions of science data packets recorded from a 100G research network at Fermi National Accelerator Laboratory. The detection performance was evaluated on traffic captured from the same research network days and weeks after the training with different types of attack flows injected. To the best of our knowledge, this is the first ML work in the area of network anomaly detection that has been validated on such extreme scale datasets. Results show that ThunderSecure can recognize science data traffic that are captured long after the training and make nearly certain detection on those with anomalous flows injected, even in the case when the anomaly-to-normal mixing ratio is 0.1%.

Gong, Qian↗
DOE OSTI2021

Improvement of the NOvA Near Detector Event Reconstruction and Primary Vertexing through the Application of Machine Learning Methods

The purpose of this work is to examine the application of a deep learning model in event reconstruction of neutrino interactions. The challenges faced in event reconstruction include the placement of an accurate primary neutrino interaction vertex which is used to support the particle track and prong algorithms. The result of accurate primary vertex ensures all particles involved in a neutrino interaction are included. We propose a regression-based Convolutional Neural Network (CNN) method to predict the primary vertex of a particle interaction. We show that with raw two-dimensional pixel map views as input, the regression-based CNN can predict the primary vertex in all three coordinates. This work is applied as part of the NOvA (NuMI Off-axis $\nu_e$ Appearance) near detector reconstruction efforts. The primary vertex predicted by the regression-based CNN model shows promising results for future applications. This deep learning method can be extended to secondary vertexing through a Kernel Density Estimate algorithm discussed in this work.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

A VOI Web Application for Distinct Geothermal Domains: Statistical Evaluation of Different Data Types within the Great Basin

The Great Basin region contains different domains that have different structural and hydrothermal flow patterns. Depending on the characteristics of these patterns, certain data types may be more successful at detecting hidden geothermal resources. In this paper, we quantitatively evaluate if certain data types are more successful in certain domains. Given different aquifer, strain and structural conditions, we explore which data types statistically reveal positively labeled geothermal sites. We utilize value of information (VOI) metrics to help quantify the reliability of data types to discriminate against "positive" and "negative" labeled geothermal sites. We also evaluate how kernel density estimation can help generalize the statistics that inform VOI, which is necessary given the limited data in geothermal exploration. Except for the Carbonate Aquifer, the highest ranking of the Vimperfect is the Local Structural Setting. Next, the slip and dilation tendency is first for Carbonate Aquifer and second for Central Nevada Seismic Belt and Western Great Basin. For the Carbonate Aquifer, heat flow is has the lowest Vimperfect value compared to the other three domains, which is consistent with the understanding of how heat flow measurements are masked by regional groundwater flow.

Bayesian analysis↗