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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
NASA NTRS1983

Characteristics of thermal and suprathermal ions associated with the dayside plasma trough as measured by the dynamics explorer retarding ion mass spectrometer

The thermal and suprathermal ion populations present in the refilling regions after a magnetic storm are examined using retarding ion mass spectrometer (RIMS) data from the Dynamics Explorer 1 spacecraft. The RIMS instrument is described, and data are presented and discussed in detail for the outer plasmasphere, plasmapause, depleted dayside magnetosphere, and dayside cusp. Three distinct populations were observed: thermal ions, warm anisotropic plasma, and the polar wind. The characteristics of these populations are considered, including the densities, temperatures, and density ratios. Aspects of the ionospheric plasma outflow are discussed, including the field-aligned flow speed, the ionospheric plasma escape flux, plasmaspheric refilling, and wave-particle phenomena.

Sojka, J. J.↗
NASA NTRS2016

Genelab: Scientific Partnerships and an Open-Access Database to Maximize Usage of Omics Data from Space Biology Experiments

NASA's mission includes expanding our understanding of biological systems to improve life on Earth and to enable long-duration human exploration of space. The GeneLab Data System (GLDS) is NASA's premier open-access omics data platform for biological experiments. GLDS houses standards-compliant, high-throughput sequencing and other omics data from spaceflight-relevant experiments. The GeneLab project at NASA-Ames Research Center is developing the database, and also partnering with spaceflight projects through sharing or augmentation of experiment samples to expand omics analyses on precious spaceflight samples. The partnerships ensure that the maximum amount of data is garnered from spaceflight experiments and made publically available as rapidly as possible via the GLDS. GLDS Version 1.0, went online in April 2015. Software updates and new data releases occur at least quarterly. As of October 2016, the GLDS contains 80 datasets and has search and download capabilities. Version 2.0 is slated for release in September of 2017 and will have expanded, integrated search capabilities leveraging other public omics databases (NCBI GEO, PRIDE, MG-RAST). Future versions in this multi-phase project will provide a collaborative platform for omics data analysis. Data from experiments that explore the biological effects of the spaceflight environment on a wide variety of model organisms are housed in the GLDS including data from rodents, invertebrates, plants and microbes. Human datasets are currently limited to those with anonymized data (e.g., from cultured cell lines). GeneLab ensures prompt release and open access to high-throughput genomics, transcriptomics, proteomics, and metabolomics data from spaceflight and ground-based simulations of microgravity, radiation or other space environment factors. The data are meticulously curated to assure that accurate experimental and sample processing metadata are included with each data set. GLDS download volumes indicate strong interest of the scientific community in these data. To date GeneLab has partnered with multiple experiments including two plant (Arabidopsis thaliana) experiments, two mice experiments, and several microbe experiments. GeneLab optimized protocols in the rodent partnerships for maximum yield of RNA, DNA and protein from tissues harvested and preserved during the SpaceX-4 mission, as well as from tissues from mice that were frozen intact during spaceflight and later dissected on the ground. Analysis of GeneLab data will contribute fundamental knowledge of how the space environment affects biological systems, and as well as yield terrestrial benefits resulting from mitigation strategies to prevent effects observed during exposure to space environments.

bioinformatics↗
NASA NTRS2016

GeneLab: Scientific Partnerships and an Open-Access Database to Maximize Usage of Omics Data from Space Biology Experiments

NASA's mission includes expanding our understanding of biological systems to improve life on Earth and to enable long-duration human exploration of space. The GeneLab Data System (GLDS) is NASAs premier open-access omics data platform for biological experiments. GLDS houses standards-compliant, high-throughput sequencing and other omics data from spaceflight-relevant experiments. The GeneLab project at NASA-Ames Research Center is developing the database, and also partnering with spaceflight projects through sharing or augmentation of experiment samples to expand omics analyses on precious spaceflight samples. The partnerships ensure that the maximum amount of data is garnered from spaceflight experiments and made publically available as rapidly as possible via the GLDS. GLDS Version 1.0, went online in April 2015. Software updates and new data releases occur at least quarterly. As of October 2016, the GLDS contains 80 datasets and has search and download capabilities. Version 2.0 is slated for release in September of 2017 and will have expanded, integrated search capabilities leveraging other public omics databases (NCBI GEO, PRIDE, MG-RAST). Future versions in this multi-phase project will provide a collaborative platform for omics data analysis. Data from experiments that explore the biological effects of the spaceflight environment on a wide variety of model organisms are housed in the GLDS including data from rodents, invertebrates, plants and microbes. Human datasets are currently limited to those with anonymized data (e.g., from cultured cell lines). GeneLab ensures prompt release and open access to high-throughput genomics, transcriptomics, proteomics, and metabolomics data from spaceflight and ground-based simulations of microgravity, radiation or other space environment factors. The data are meticulously curated to assure that accurate experimental and sample processing metadata are included with each data set. GLDS download volumes indicate strong interest of the scientific community in these data. To date GeneLab has partnered with multiple experiments including two plant (Arabidopsis thaliana) experiments, two mice experiments, and several microbe experiments. GeneLab optimized protocols in the rodent partnerships for maximum yield of RNA, DNA and protein from tissues harvested and preserved during the SpaceX-4 mission, as well as from tissues from mice that were frozen intact during spaceflight and later dissected on the ground. Analysis of GeneLab data will contribute fundamental knowledge of how the space environment affects biological systems, and as well as yield terrestrial benefits resulting from mitigation strategies to prevent effects observed during exposure to space environments.

spaceflight↗
NASA NTRS1999

Improving Assimilated Global Climate Data Using TRMM and SSM/I Rainfall and Moisture Data

Current global analyses contain significant errors in primary hydrological fields such as precipitation, evaporation, and related cloud and moisture in the tropics. Work has been underway at NASA's Data Assimilation Office to explore the use of TRMM and SSM/I-derived rainfall and total precipitable water (TPW) data in global data assimilation to directly constrain these hydrological parameters. We found that assimilating these data types improves not only the precipitation and moisture estimates but also key climate parameters directly linked to convection such as the outgoing longwave radiation, clouds, and the large-scale circulation in the tropics. We will present results showing that assimilating TRMM and SSM/I 6-hour averaged rain rates and TPW estimates significantly reduces the state-dependent systematic errors in assimilated products. Specifically, rainfall assimilation improves cloud and latent heating distributions, which, in turn, improves the cloudy-sky radiation and the large-scale circulation, while TPW assimilation reduces moisture biases to improve radiation in clear-sky regions. Rainfall and TPW assimilation also improves tropical forecasts beyond 1 day.

Hou, Arthur Y.↗
NASA NTRS2016

CEOS WGISS Common Data Framework for WGISS Connected Data Assets

This session will explore the benefits of having such a policy framework and future steps both domestically and internationally. Speakers can highlight current work being done to improve data interoperability, how the Common Framework is relevant for other data types, other countries and multinational organizations, and considerations for data management that have yet to be addressed in the Common Framework.

common framework↗
NASA NTRS1991

Revised global model of thermosphere winds using satellite and ground-based observations

Thermospheric wind data obtained from the Atmosphere Explorer E and Dynamics Explorer 2 satellites have been combined with wind data for the lower and upper thermosphere from ground-based incoherent scatter radar and Fabry-Perot optical interferometers to generate a revision (HWM90) of the HWM87 empirical model and extend its applicability to 100 km. Comparison of the various data sets with the aid of the model shows in general remarkable agreement, particularly at mid and low latitudes. The ground-based data allow modeling of seasonal/diurnal variations, which are most distinct at midlatitudes. While solar activity variations are now included, they are found to be small and not always very clearly delineated by the current data. They are most obvious at the higher latitudes. The model describes the transition from predominately diurnal variations in the upper thermosphere to semidiurnal variations in the lower thermosphere and a transition from summer to winter flow above 140 km to winter to summer flow below. Significant altitude gradients in the wind are found to extend to 300 km at some local times and pose complications for interpretation of Fabry-Perot observations.

Hedin, A. E.↗
NASA NTRS2015

Reinforcement Learning with Autonomous Small Unmanned Aerial Vehicles in Cluttered Environments

We present ongoing work in the Autonomy Incubator at NASA Langley Research Center (LaRC) exploring the efficacy of a data set aggregation approach to reinforcement learning for small unmanned aerial vehicle (sUAV) flight in dense and cluttered environments with reactive obstacle avoidance. The goal is to learn an autonomous flight model using training experiences from a human piloting a sUAV around static obstacles. The training approach uses video data from a forward-facing camera that records the human pilot's flight. Various computer vision based features are extracted from the video relating to edge and gradient information. The recorded human-controlled inputs are used to train an autonomous control model that correlates the extracted feature vector to a yaw command. As part of the reinforcement learning approach, the autonomous control model is iteratively updated with feedback from a human agent who corrects undesired model output. This data driven approach to autonomous obstacle avoidance is explored for simulated forest environments furthering autonomous flight under the tree canopy research. This enables flight in previously inaccessible environments which are of interest to NASA researchers in Earth and Atmospheric sciences.

Tran, Loc↗
NASA NTRS2018

Decadal Seasonal Shifts of Precipitation and Temperature in TRMM and AIRS Data

We present results from an analysis of seasonal phase shifts in the global precipitation and surface temperatures. We use data from the TRMM (Tropical Rainfall Measuring Mission) Multi-satellite Precipitation Algorithm (TMPA), and the Atmospheric Infrared Sounder (AIRS) on Aqua satellite, all hosted at NASA Goddard Earth Science Data and Information Services Center (GES DISC). We explore the information content and data usability by first aggregating daily grids from the entire records of both missions to pentad (5-day) series which are then processed using Singular Value Decomposition approach. A strength of this approach is the normalized principal components that can then be easily converted from real to complex time series. Thus, we can separate the most informative, the seasonal, components and analyze unambiguously for potential seasonal phase drifts. TMPA and AIRS records represent correspondingly 20 and 15 years of data, which allows us to run simple “phase learning†from the first 5 years of records and use it as reference. The most recent 5 years are then phase-compared with the reference. We demonstrate that the seasonal phase of global precipitation and surface temperatures has been stable in the past two decades. However, a small global trend of delayed precipitation, and earlier arrival of surface temperatures seasons, are detectable at 95% confidence level. Larger phase shifts are detectable at regional level, in regions recognizable from the Eigen vectors to having strong seasonal patterns. For instance, in Central North America, including the North American Monsoon region, confident phase shifts of 1-2 days per decade are detected at 95% confidence level. While seemingly symbolic, these shifts are indicative of larger changes in the Earth Climate System. We thus also demonstrate a potential usability scenario of Earth Science Data Records curated at the NASA GES DISC in partnership with Earth Science Missions.

surface temperatures↗
NASA NTRS2019

Citizen Science Twitter Data Management for Earth Science Applications

Social media data can provide useful real-time and historical information relating to the natural world, but managing this data poses challenges. Scientists at GES DISC are exploring the potential of Twitter data to augment precipitation data from the Global Precipitation Measurement (GPM) mission. However, the format of Twitter data is unconventional in the context of NASA data centers, resulting in frustration for scientists who need to work with the data. This study investigated procedures and standards needed to properly manage Twitter data to make them compatible with these data centers. After comparing databases, the study found that the MongoDB database was best suited for the storage of raw Twitter data due to its flexibility, ability to be accessed by multiple users, and querying functionality. The study used the Python package Zarr to transform processed Twitter data into a gridded format similar to that of satellite data. Each Tweet was mapped onto a time-space grid; each grid location contained information about Tweet attributes and precipitation. The study developed a pipeline for downloading, storing, and gridding Twitter data and transformed Twitter data into an understandable format for users of NASA satellite data.

Li, Rachel↗
NASA NTRS1962

United States Space Explorations 1958

The film describes preparation and launch of five satellites and two space probes during 1958. On January 31, a Jupiter vehicle launched Explorer I into space. Data from this satellite was used to identify the van Allen radiation belts. On March 17, a Vanguard I rocket launched an Earth satellite with solar batteries. Data from the mission was used to determine that the Earth is slightly pear-shaped. On March 26, Explorer III was launched to further study the van Allen belts, micrometeoroid impacts, and internal and external temperatures. Explorer IV, launched on July 26, was intended to study radiation and temperature data. A lunar probe, ABLE I, was intended to measure radiation, magnetic fields of Earth and the Moon, density of micrometeoric matter, and internal temperatures. A four-stage rocket was used in the launch. However, a turbo-pump failed and the liquid oxygen pump stopped, resulting in a failed mission. On October 10, Pioneer I was launched by an ABLE vehicle. First and second stage velocity was less than desired and the probe did not leave Earth orbit. Attempts to attain escape velocity were unsuccessful. On December, a Jupiter boost vehicle was used to launch Juno II, with Pioneer III as the payload. Escape velocity was reached and Pioneer III left Earth's atmosphere. Failed launches, such as those of Vanguard boost vehicles and several Explorer satellites, also added to scientific knowledge.

Source record↗
NASA NTRS1995

Investigation of the role of gravity waves in the generation of equatorial bubbles

The following areas of interest in this progress report are: (1) the continuation of software development in the examination of F-region gravity-wave power using in-situ data from the Atmosphere Explorer (AE-E); (2) the inquiry into the use of the San Marco data for the study of the initiation and growth of bubbles, particularly when the satellite passes through the early evening hours at relatively high altitudes, and the development of bubbles using not only the San Marco data but includes the use of airglow observations made in Hawaii; and (3) the promising development in the observation of distinct well formed waves at about 400 km altitude in the equatorial region. These waves look very much like waves seen over the polar cap that are attributed to internal gravity waves in the neutral atmosphere driving ionization up and down the magnetic field lines. These equatorial waves show no modulation of the total ion concentration.

Johnson, Francis S.↗
NASA NTRS2023

NLSP: NASA Life Sciences Portal

NASA’s Life Sciences Ports (NLSP) serves the scientific community by providing curated data from space life science experiment. The Human Research Program (HRP) with the help of NLSP is currently transforming their life sciences data archive systems and processes to improve compliance with the FAIR principles. Some of these improvements will at the same time support the twin pillars of Open Science: transparency of methods and reproducibility of results. This video is a high level overview of the NLSP for existing and new users.

Life Sciences data↗
NASA NTRS1997

Restoration and Archiving of Data from the Plasma Composition Experiment on the International Sun-Earth Explorer One (ISEE 1)

The objective of this project has been to complete the archiving of energetic (10 eV/epsilon - 18 keV/epsilon) ion composition data from the Lockheed Plasma Composition Experiment on the International Sun-Earth Explorer One (ISEE 1) satellite, using a particular data format that had previously been approved by NASA and the NSSDC. That same format, a combination of ion velocity moments and differential flux spectra, had been used in 1991 to archive, at the NSSDC, the first 28 months (the "Prime" period of ISEE investigations) of data from the Lockheed instrument under NASA Contract NAS5-33047. With the completion of this project, the almost 4 1/2-year time span of these unique data is now covered by a very compact set, approximately 1 gigabyte in total, of electronic files with physical quantities, all in ASCII. The files are organized by data type and time of data acquisition, in Universal Time, and named according to year and day of year. Each calendar day has five separate files (five types of data), the lengths of which vary from day to day, depending on the instrument mode of operation. The data format and file structure are described in detail in appendices 1 and 2. The physical medium consists of high-density (6250 cpi) 9-track magnetic tapes, complemented by a set of hardcopy line plots of certain plasma parameters. In this case there are five tapes, to be added to the six previous ones from 1991, and 25 booklets of plots, one per month, to be added to the previous 28. The tapes, including an extra standard-density (1600 cpi) tape with electronic versions of the Data User's Guide and self-guiding VAX/VMS command files, and the hardcopy plots are being boxed for shipment to the NSSDC.

Lennartsson, O. W.↗
NASA NTRS1984

Propagation as a mechanism of reorientation of the Juan de Fuca ridge

A revised model is presented of the tectonic evolution of the Juan de Fuca ridge by propagating rifting. The new model has three different relative rotation poles, covering the time intervals 17.0-8.5 Ma, 8.5-5.0 Ma, and 5.0 Ma to the present. The rotation pole shifts at 8.5 and 5.0 Ma imply clockwise shifts in the direction of relative motion of 10 deg to 15 deg. At each of these shifts, the pattern of propagation reorganizes, and the new ridges formed by propagation are at an orientation closer to orthogonal to the new direction of motion than the orientation of the preexisting ridges. The model, containing a total of seven propagation sequences, shows excellent agreement with the isochrons inferred from the magnetic anomaly data, except in areas complicated by the separate Explorer and Gorda plates. The agreement between model and data near the Explorer plate breaks down abruptly at an age of about 5 Ma, indicating that the probable cause of the rotation pole shift at that time was the separation of the Explorer plate from the Juan de Fuca plate.

Wilson, D. S.↗