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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Secondary Ion Mass Spectroscopy

Secondary Ion mass Spectroscopy (SIMS), as the name suggests, involves characterizing metallic and other materials trough the spectroscopic analysis of secondary ions emanating from the surface of the material to be characterized by the impact of the high energy primary ions. The primary ion source including the choice of its gun, voltage and current can be selected and used depending on the purpose of the analysis. In most instruments more than one primary ion gun is lined up to the sample stage and can be activated with selected accelerating parameters (voltage and beam intensity). The impingement of primary ions on to the sample surface generates positive, negative or neutral ions, electrons, atoms and atomic clusters. Majority of these sample fragments being neutral, could not be utilized as such fragments cannot be manipulated through the use of electromagnetic or electrostatic lenses. Secondary ions that are positively or negatively charged possess large variation in velocity, charge, and mass. These ionic fragments eventually travel through a system of several lenses in very high vacuum to reach detector/counter. Relative amounts of alloying elements or impurities in an alloy can be calculated from the counts of related ions accumulated in the detector/counter.

Panda, Binayak↗
NASA NTRS2021

Computational Inference of Vibratory System with Incomplete Modal Information Using Parallel, Interactive and Adaptive Markov Chains

Inverse analysis of vibratory system is an important subject in fault identification, model updating, and robust design and control. It is challenging subject because 1) the problem is oftentimes underdetermined while the measurements are limited and/or incomplete; 2) many combinations of parameters may yield results that are similar with respect to actual response measurements; and 3) uncertainties inevitably exist. The aim of this research is to leverage upon computational intelligence through statistical inference to facilitate an enhanced, probabilistic framework using incomplete modal response measurement. This new framework is built upon efficient inverse identification through optimization, whereas Bayesian inference is employed to account for the effect of uncertainties. To overcome the computational cost barrier, we adopt Markov chain Monte Carlo (MCMC) to characterize the target function/distribution. Instead of using single Markov chain in conventional Bayesian approach, we develop a new sampling theory with multiple parallel, interactive and adaptive Markov chains and incorporate it into Bayesian inference. This can harness the collective power of these Markov chains to realize the concurrent search of multiple local optima. The number of required Markov chains and their respective initial model parameters are automatically determined via Monte Carlo simulation-based sample pre-screening followed by K-means clustering analysis. These enhancements can effectively address the aforementioned challenges in finite element inverse analysis. The validity of this framework is systematically demonstrated through case studies.

K Zhou↗
NASA NTRS2003

Premotor neurons encode torsional eye velocity during smooth-pursuit eye movements

Responses to horizontal and vertical ocular pursuit and head and body rotation in multiple planes were recorded in eye movement-sensitive neurons in the rostral vestibular nuclei (VN) of two rhesus monkeys. When tested during pursuit through primary eye position, the majority of the cells preferred either horizontal or vertical target motion. During pursuit of targets that moved horizontally at different vertical eccentricities or vertically at different horizontal eccentricities, eye angular velocity has been shown to include a torsional component the amplitude of which is proportional to half the gaze angle ("half-angle rule" of Listing's law). Approximately half of the neurons, the majority of which were characterized as "vertical" during pursuit through primary position, exhibited significant changes in their response gain and/or phase as a function of gaze eccentricity during pursuit, as if they were also sensitive to torsional eye velocity. Multiple linear regression analysis revealed a significant contribution of torsional eye movement sensitivity to the responsiveness of the cells. These findings suggest that many VN neurons encode three-dimensional angular velocity, rather than the two-dimensional derivative of eye position, during smooth-pursuit eye movements. Although no clear clustering of pursuit preferred-direction vectors along the semicircular canal axes was observed, the sensitivity of VN neurons to torsional eye movements might reflect a preservation of similar premotor coding of visual and vestibular-driven slow eye movements for both lateral-eyed and foveate species.

Non-NASA Center↗
NASA NTRSDate not supplied

Perturbations in Brain Functional Connectivity Patterns After Waking From Slow Wave Sleep Under Different Cognitive States

Sleep inertia refers to the state of transition between sleep and wake characterized by impaired alertness, confusion, and reduced cognitive and behavioral performance. While the behavioral symptoms of sleep inertia are well described, the neurological changes that lead to this state remain elusive. Here, to understand the state of sleep inertia and the reorganization that the brain undergoes, we took a graph theoretical approach and compared the EEG derived brain connectivity patterns before sleep and after waking up while participants (n = 10) performed multiple tasks that differed in cognitive complexities. We focused on how the degree and the clustering coefficient of brain regions (EEG sensors) change immediately after participants wake up from slow wave sleep. During a psychomotor vigilance task (PVT), designed to assess vigilant attention, we find that the brain regions with strong network connectivity (degree) before sleep show a reduction in connectivity after waking. In contrast, those with low connectivity before sleep have greater connectivity after waking. The regions that undergo these changes are specific to each participant and these findings are unique to the beta frequency range, which plays a key role in sensorimotor functioning and preserving the current state of the brain. Moreover, in tasks that required inhibitory control and arithmetic reasoning, we found that only regions with weak connectivity before sleep exhibited more connections after waking, but regions with high connectivity prior to sleeping remained unchanged, highlighting task specific effects. Furthermore, we find that during the PVT, the clustering coefficient within low frequency oscillations of the brain is reduced upon waking while it remains unchanged during other tasks. These results suggest that the connections between regions that are lost after abrupt awakening can be reallocated to other regions in order to renormalize the brain. However, this response may only be evident during specific cognitive states and may be more nuanced during complex task performance.

sleep inertia↗
NASA NTRS2017

Structure in the 3D Galaxy Distribution: III. Fourier Transforming the Universe: Phase and Power Spectra

We demonstrate the effectiveness of a relatively straightforward analysis of the complex 3D Fourier transform of galaxy coordinates derived from redshift surveys. Numerical demonstrations of this approach are carried out on a volume-limited sample of the Sloan Digital Sky Survey redshift survey. The direct unbinned transform yields a complex 3D data cube quite similar to that from the Fast Fourier Transform (FFT) of finely binned galaxy positions. In both cases deconvolution of the sampling window function yields estimates of the true transform. Simple power spectrum estimates from these transforms are roughly consistent with those using more elaborate methods. The complex Fourier transform characterizes spatial distributional properties beyond the power spectrum in a manner different from (and we argue is more easily interpreted than) the conventional multi-point hierarchy. We identify some threads of modern large scale inference methodology that will presumably yield detections in new wider and deeper surveys.

Galactic clusters↗
NASA NTRSDate not supplied

Optimizing Altitude Sampling and Sensitivity with the Goldstone Orbital Debris Radar

The NASA Orbital Debris Program Office (ODPO) has used the Goldstone Orbital Debris Radar (Goldstone) since 1993 to characterize orbital debris (OD) in low Earth orbit too small to be tracked by the U.S. Space Surveillance Network. Operated by NASA’s Jet Propulsion Laboratory, Goldstone can measure OD as small as 3 mm at 1000 km altitude and lower. Goldstone is a bistatic radar that for 25 years used Deep Space Station (DSS)-14 as a transmitter and DSS-15 as a receiver. In early 2018, DSS-15 was decommissioned and replaced with DSS-25 (and occasionally DSS-26) of the Deep Space Network Apollo Cluster. The increased baseline between DSS-14 and DSS-25 significantly reduced the instantaneous altitude coverage of the bistatic beam overlap. Initial measurements in 2018 were focused around 800 km, which has approximately the highest flux of sub-centimeter debris. In 2019, DSS-14 was offline for maintenance, and the ODPO designed an annual survey observation plan to efficiently sample altitudes from 700 km to 1000 km, since many NASA satellites fly in this range. This paper discusses the observation plan, including the development of the pointings, a refinement of the altitudes of interest, and an analysis of the effects of random pointing errors on beam overlap. Additionally, results from measurements taken in 2020 and 2021 are presented, showing that not only is the observation plan effective at sampling 700 km to 1000 km altitude, but it is also producing the most sensitive terrestrial radar measurements at these altitudes to date.

James Murray↗
NASA NTRS2023

Optimizing Altitude Sampling and Sensitivity with the Goldstone Orbital Debris Radar

The NASA Orbital Debris Program Office (ODPO) has used the Goldstone Orbital Debris Radar (Goldstone) since 1993 to characterize orbital debris (OD) in low Earth orbit too small to be tracked by the U.S. Space Surveillance Network. Operated by NASA’s Jet Propulsion Laboratory, Goldstone can measure OD as small as 3 mm at 1000 km altitude and lower. Goldstone is a bistatic radar that for 25 years used Deep Space Station (DSS)-14 as a transmitter and DSS-15 as a receiver. In early 2018, DSS-15 was decommissioned and replaced with DSS-25 (and occasionally DSS-26) of the Deep Space Network Apollo Cluster. The increased baseline between DSS-14 and DSS-25 significantly reduced the instantaneous altitude coverage of the bistatic beam overlap. Initial measurements in 2018 were focused around 800 km, which has approximately the highest flux of sub-centimeter debris. In 2019, DSS-14 was offline for maintenance, and the ODPO designed an annual survey observation plan to efficiently sample altitudes from 700 km to 1000 km, since many NASA satellites fly in this range. This paper discusses the observation plan, including the development of the pointings, a refinement of the altitudes of interest, and an analysis of the effects of random pointing errors on beam overlap. Additionally, results from measurements taken in 2020 and 2021 are presented, showing that not only is the observation plan effective at sampling 700 km to 1000 km altitude, but it is also producing the most sensitive terrestrial radar measurements at these altitudes to date.

James Murray↗
NASA NTRS1986

High-velocity interstellar gas in the line of sight to the Wolf-Rayet star HD 50896

The large shell of interstellar gas (IG) discovered toward HD 50896 by Heckathorn and Fesen (1984) is characterized on the basis of high-dispersion IUE SWP and LWR spectra of 19 objects located within 4 deg of HD 50896 (but outside the optical ring nebula S308) at distances 0.6-2.9 kpc (compared to 1.5 kpc for HD 50896). The IG is found to have two components (at velocities -80 and -125 km/s), diameter 90 pc or greater, and distance 1.0 + or - 0.2 kpc, demonstrating that it is not related to HD 50896 and suggesting that it is a highly evolved supernova remnant associated with cluster Cr 121.

Nichols-Bohlin, J.↗
NASA NTRS2019

The Impact of Stripped Cores on the Frequency of Earth-size Planets in the Habitable Zone

The frequency of Earth-size planets in the habitable zone(HZ)of Sun-like stars, hereafter, is a key parameter to evaluate the yield of nearby Earth analogs that can be detected and characterized by future missions. Yet, this value is poorly constrained as there are no reliable exoplanet candidates in the HZ of Sun-like stars in the Kepler field. Here, we show that extrapolations relying on the population of small(<1.8R), short-period(<25 days)planets bias to large values. As the radius distribution at short orbital periods is strongly affected by atmospheric loss, we reevaluate using exoplanets at larger separations. We find that drops considerably, to values of only5% - 10%. Observations of young(<100 Myr)clusters can probe short-period sub-Neptunes that still retain most of their envelope mass. As such, they can be used to quantify the contamination of sub-Neptunes to the population of Kepler short-period small planets and aid in more reliable estimates of.

Pascucci, Ilaria↗
NASA NTRS1987

Spectral characteristics and the extent of paleosols of the Palouse formation

Thematic mapping data was analyzed and verified by comparison to previously gathered transect samples and to aerial photographs. A bare-soil field with exposed paleosols characterized by slight enrichment of iron was investigated. Spectral relationships were first investigated statistically by creating a data set with DN values spatially matched as nearly as possible to field sample points. Chemical data for each point included organic carbon, free iron oxide, and amorphous iron content. The chemical data, DN values, and various band ratios were examined with the program package Statistix in order to find the combinations of reflectance data most likely to show a relationship which would dependably separate the exposed paleosols from the other soils. Cluster analysis and Fastclas classification procedures were applied to the most promising of the band ratio combinations.

Frazier, B. E.↗
NASA NTRS1991

Group evaporation

Liquid fuel combustion process is greatly affected by the rate of droplet evaporation. The heat and mass exchanges between gas and liquid couple the dynamics of both phases in all aspects: mass, momentum, and energy. Correct prediction of the evaporation rate is therefore a key issue in engineering design of liquid combustion devices. Current analytical tools for characterizing the behavior of these devices are based on results from a single isolated droplet. Numerous experimental studies have challenged the applicability of these results in a dense spray. To account for the droplets' interaction in a dense spray, a number of theories have been developed in the past decade. Herein, two tasks are examined. One was to study how to implement the existing theoretical results, and the other was to explore the possibility of experimental verifications. The current theoretical results of group evaporation are given for a monodispersed cluster subject to adiabatic conditions. The time evolution of the fluid mechanic and thermodynamic behavior in this cluster is derived. The results given are not in the form of a subscale model for CFD codes.

Shen, Hayley H.↗
NASA NTRS2000

Formation Flying In Highly Elliptical Orbits Initializing the Formation

In this paper several methods are examined for initializing formations in which all spacecraft start in a common elliptical orbit subsequent to separation from the launch vehicle. The tetrahedron formation used on missions such as the Magnetospheric Multiscale (MMS), Auroral Multiscale Midex (AMM), and Cluster is used as a test bed Such a formation provides full three degrees-of-freedom in the relative motion about the reference orbit and is germane to several missions. The type of maneuver strategy that can be employed depends on the specific initial conditions of each member of the formation. Single-impulse maneuvers based on a Gaussian variation-of-parameters (VOP) approach, while operationally simple and intuitively-based, work only in a limited sense for a special class of initial conditions. These 'tailored' initial conditions are characterized as having only a few of the Keplerian elements different from the reference orbit. Attempts to achieve more generic initial conditions exceed the capabilities of the single impulse VOP. For these cases, multiple-impulse implementations are always possible but are generally less intuitive than the single-impulse case. The four-impulse VOP formalism discussed by Schaub is examined but smaller delta-V costs are achieved in our test problem by optimizing a Lambert solution.

Mailhe, Laurie↗
NASA NTRS2020

Cluster Analysis of Thermal Icequakes Using the Seismometer to Investigate Ice and Ocean Structure (SIIOS): Implications for Ocean World Seismology

Ocean Worlds are of high interest to the planetary community due to the potential habitability of their subsurface oceans. Over the next few decades several missions will be sent to ocean worlds including the Europa Clipper, Dragonfly, and possibly a Europa lander. The Dragonfly and Europa lander missions will carry seismic payloads tasked with detecting and locating seismic sources. The Seismometer to Investigate Ice and Ocean Structure (SIIOS) is a NASA PSTAR funded project that investigates ocean world seismology using terrestrial analogs. The goals of the SIIOS experiment include quantitatively comparing flight-candidate seismometers to traditional instruments, comparing single-station approaches to a small-aperture array, and characterizing the local seismic environment of our field sites. Here we present an analysis of detected local events at our field sites at Gulkana Glacier in Alaska and in Northwest Greenland approximately 80 km North of Qaanaaq, Greenland. Both field sites passively recorded data for about two weeks. We deployed our experiment on Gulkana Glacier in September 2017 and in Greenland in June 2018. At Gulkana there was a nearby USGS weather station which recorded wind data. Temperature data was collected using the MERRA satellite. In Greenland we deployed our own weather station to collect temperature and wind data. Gulkana represents a noisier and more active environment. Temperatures fluctuated around 0°C, allowing for surface runoff to occur during the day. The glacier had several moulins, and during deployment we heard several rockfalls from nearby mountains. In addition to the local environment, Gulkana is located close to an active plate boundary (relative to Greenland). This meant that there were more regional events recorded over two weeks, than in Greenland. Greenland’s local environment was also quieter, and less active. Temperatures remained below freezing. The Greenland ice was much thicker than Gulkana (~850 m versus ~100 m) and our stations were above a subglacial lake. Both conditions can reduce event detections from basal motion. Lastly, we encased our Greenland array in an aluminum vault and buried it beneath the surface unlike our array in Gulkana where the instruments were at the surface and covered with plastic bins. The vault further insulated the array from thermal and atmospheric events.

Marusiak, A. G.↗
NASA NTRS1991

An inhomogeneous reference catalogue of identified intervening heavy element systems in spectra of QSOs

Identifications of heavy element line systems, observed in spectra of quasi-stellar objects between 1965 and 1989 inclusive, are collected and tabulated with references. Each system is assigned a quality grade based on the apparent reliability of the data. The highest quality systems are used to characterize the absorbers as a sample. A decrease in C IV line strength with redshift (z), and the corresponding decrease in line density per unit z with z are confirmed. The weakest C IV systems, at high z, are accompanied by relatively stronger Si IV lines, compared to the relative line strengths at low z. The space density of systems with strong lines of first ions is nearly independent of z below z = 3, though it drops at z greater than 3. Preliminary tests for quasar lensing by intervening systems and for clustering of absorbers on large scales are presented.

York, Donald G.↗
NASA NTRS2022

Characterizing Student-Driven Research Investigations Contributed to the GLOBE Program Citizen Science Initiative in a Formal Education Context

The Global Learning and Observations to Benefit the Environment (GLOBE) Program offers citizen science opportunities to participants of all ages, with a focus on youth in formal classroom contexts. This study uses student investigation research reports and posters submitted to the 2018 International Virtual Science Symposium (IVSS) and Student Research Symposium (SRS) as testbeds for characterizing student-driven Earth system citizen science investigations. Secondarily, this study aimed to capture GLOBE’s alignment to existing citizen science outcomes frameworks in the literature, which have primarily focused on adults and non-formal settings. Based on a literature review, the evaluation team identified 89 potential characteristics in 27 categories to typify investigations from both formal education and citizen science perspectives. We coded the artifacts from 207 student projects, conducted quantitative analysis of frequencies, and performed a semantic network analysis. By using this networking approach, we conceptually mapped several clusters of co-occurring characteristics, defining a descriptive framework for GLOBE projects. We identified three tiers of citizen science projects, increasing in the sophistication of participants’ demonstrated science practices. The framework includes additional components that reflect student citizen scientists’ thoughtfulness and connection to context as well as their projects’ reflection of their motivation and self efficacy. Through these findings, we have identified areas where student citizen scientists would benefit from further support, and suggest here further research to incorporate the experiences of students into the broader understanding of citizen science outcomes.

network analysis↗
NASA NTRSDate not supplied

ArcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

machine learning↗
NASA NTRSDate not supplied

arcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

machine learning↗
NASA NTRSDate not supplied

arcjetCV: automating recession extraction from video

Arc jet Computer Vision (arcjetCV)[1][2] is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗