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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 145 records · Page 8
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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, sting arm motion, 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.

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: 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 NTRS1979

GEOS 3 STDN S band Doppler tracking investigation

GEOS 3S Doppler band and laser ranging data, acquired from August 1975 to March 1976 in the spacecraft altimeter calibration area, are examined. An evaluation of two-way and three-way Doppler data, for the positioning of Spaceflight Tracking and Data Network S band stations is presented, as well as the Goddard Space Flight Center laser system that is used to reference the exact position of the Doppler stations. The two-way and three-way Doppler tracking devices, situated at Rosman and Bermuda, have yielded data for the recovery of GEOS 3 arc height with an uncertainty of only 1 m. Attention is given to the effects of beacon signal frequency instability, controlled by a temperature sensitive auxiliary crystal oscillator on board the spacecraft, and to the one-way range rate tracking noise that was found to be within a range of 2 to 10 cm/s. 1- and 2-way passes and their different arc meters are graphed, showing the Doppler tracking interval. It was concluded that other accurate computations and recovery of station coordinates could be performed employing tracking data from S band stations.

Rosenbaum, B.↗
NASA NTRS1992

Contribution of Doppler and interferometric tracking during the Magellan approach to Venus

On May 4, 1989, the Magellan spacecraft began its 463 day and 1.3 billion km earth to Venus interplanetary cruise. Magellan's Venus approach trajectory required prediction accuracy of 6.1 seconds in arrival time and 126 km in the position of closest approach to achieve a desired Venus orbit. Data collection from Magellan's SAR required an elliptical orbit with an inclination between 84 deg and 86 deg to the Venus equator, a period between 3.1 and 3.3 hours, a periapsis altitude within 275 km and 325 km and a latitude of periapsis between 0 deg and 10 deg North. Predictions of Magellan's arrival time and closest approach to Venus were derived from cruise trajectories determined exclusively from coherent two-way Doppler using both S-band (2.3 GHz) uplink/downlink and X-band (8.4 GHz) uplink/downlink and X-band spacecraft-quasar interferometric delay data. Analysis of data reduction strategies employing various data arc lengths and data combinations shows that interferometric tracking data, used in conjunction with Doppler, improved trajectory solution accuracy and robustness.

Graat, E. J.↗
NASA NTRS1994

A geopotential model from satellite tracking, altimeter, and surface gravity data: GEM-T3

An improved model of Earth's gravitational field, Goddard Earth Model T-3 (GEM-T3), has been developed from a combination of satellite tracking, satellite altimeter, and surface gravimetric data. GEM-T3 provides a significant improvement in the modeling of the gravity field at half wavelengths of 400 km and longer. This model, complete to degree and order 50, yields more accurate satellite orbits and an improved geoid representation than previous Goddard Earth Models. GEM-T3 uses altimeter data from GEOS 3 (1975-1976), Seasat (1978) and Geosat (1986-1987). Tracking information used in the solution includes more than 1300 arcs of data encompassing 31 different satellites. The recovery of the long-wavelength components of the solution relies mostly on highly precise satellite laser ranging (SLR) data, but also includes Tracking Network (TRANET) Doppler, optical, and satellite-to-satellite tracking acquired between the ATS 6 and GEOS 3 satellites. The main advances over GEM-T2 (beyond the inclusion of altimeter and surface gravity information which is essential for the resolution of the shorter wavelength geoid) are some improved tracking data analysis approaches and additional SLR data. Although the use of altimeter data has greatly enhanced the modeling of the ocean geoid between 65 deg N and 60 deg S latitudes in GEM-T3, the lack of accurate detailed surface gravimetry leaves poor geoid resolution over many continental regions of great tectonic interest (e.g., Himalayas, Andes). Estimates of polar motion, tracking station coordinates, and long-wavelength ocean tidal terms were also made (accounting for 6330 parameters). GEM-T3 has undergone error calibration using a technique based on subset solutions to produce reliable error estimates. The calibration is based on the condition that the expected mean square deviation of a subset gravity solution from the full set values is predicted by the solutions' error covariances. Data weights are iteratively adjusted until this condition for the error calibration is satisfied. In addition, gravity field tests were performed on strong satellite data sets withheld from the solution (thereby ensuring their independence). In these tests, the performance of the subset models on the withheld observations is compared to error projections based on their calibrated error covariances. These results demonstrate that orbit accuracy projections are reliable for new satellites which were not included in GEM-T3.

Lerch, F. J.↗
NASA NTRS1997

Few arc-minute and keV resolutions with the TIGRE Compton telescope

The tracking and imaging gamma ray experiment (TIGRE) Compton telescope concept can provide an angular resolution of a few arcmin, an energy resolution of a few keV and high sensitivity, while providing the wide field of view necessary for surveying and monitoring observations. Silicon and CdZnTe strip detectors are used to detect Compton pair events and determine their incident directions and energies. Above 400 keV, Compton recoil electrons are tracked through successive layers of thin silicon strip detectors. Compton scattered photons are detected with CdZnTe strip detectors. Pair electrons and positrons are tracked to provide high sensitivity observations in the 10 to 100 MeV range. Polarization studies are performed with large angle Compton scatter events. The TIGRE concept and development status are described.

Zych, A.↗
NASA NTRS1992

An improved model of the Earth's gravity field - GEM-T3

An improved model of the Earth's gravitational field is developed from a combination of conventional satellite tracking, satellite altimeter measurements, and surface gravimetric data (GEM-T3). This model gives improved performance for the computation of satellite orbital effects as well as a superior representation of the geoid from that achieved in any previous Goddard Earth Model. The GEM-T3 model uses altimeter data directly to define the orbits, geoid, and dynamic height fields. Altimeter data acquired during the GEOS-3 (1975-1976), SEASAT (1978), and GEOSAT (1986-1987) missions were used to compute GEM-T3. In order to accommodate the non-gravitational signal mapped by these altimeters, spherical harmonic models of the dynamic height of the ocean surface were recovered for each mission simultaneously with the gravitational field. The tracking data utilized in the solution includes more than 1300 arcs of data encompassing 31 different satellites. The observational data base is highly dependent on SLR, but also includes TRANET Doppler, optical, S-Band average range-rate and satellite-to-satellite tracking acquired between ATS-6 and GEOS-3. The GEM-T3 model has undergone extensive error calibration.

Nerem, R. S.↗
NASA NTRS1981

Cometary ephemerides - needs and concerns

With the use of narrow field-of-view instrumentation on faint comets, the accuracy requirements upon computed ephemerides are increasing. It is not uncommon for instruments with a one arc minute field-of-view to be tracking a faint comet that is not visible without a substantial integration time. As with all ephemerides of solar syste objects, the computed motion and reduction of these observations, the computed motion of a comet is further depenent upon effects related to the comet's activity. Thus, the ephemeris of an active comet is corrupted by both observational errors and errors due to the comet's activity.

Yeomans, D. K.↗
NASA NTRS1983

Radio metric orbit determination for the Giotto mission to Comet Halley

An international fleet of five spacecraft will fly past Comet Halley as it travels through the inner solar system in early 1986. This paper discusses orbit determination problems associated with the Giotto spacecraft, sponsored by the European Space Agency. The large number of spin axis precession maneuvers required to maintain the desired spacecraft attitude creates a new kind of radio metric orbit determination problem for this mission. This paper investigates the accuracy with which the Giotto spacecraft orbit can be determined relative to the earth or the sun, and establishes the sensitivity of this accuracy to the selection of the parameters to be estimated, the form of estimator used, the number of tracking stations employed, the length of the data arc, the selection of data types processed, and the levels of various error sources.

Wood, L. J.↗
NASA NTRSDate not supplied

ArcjetCV: Automating Arc Jet Analysis

Arc jet Computer Vision (arcjetCV) 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↗
NASA NTRS1973

Determination of the relativistic time delay for Mariner 9: A status report on the JPL analysis of normal points

The procedure which has been chosen to accomplish a reduction of the Mariner 9 tracking data makes use of Doppler data over relatively short arcs of about one to nine orbital revolutions to determine the orbit of the spacecraft about Mars. Next, with the knowledge of the Mars-centered orbit, it is possible to use the range data to the spacecraft to determine the distance between the center of earth and the center of Mars for each short interval of tracking data. These reduced range measurements between the centers of the two planets make up the basic data for the relativity test.

Anderson, J. D.↗
NASA NTRS1979

Utilization of satellite-satellite tracking data for determination of the geocentric gravitational constant (GM)

Range rate tracking of GEOS 3 through the ATS 6 satellite was used, along with ground tracking of GEOS 3, to estimate the geocentric gravitational constant (GM). Using multiple half day arcs, a GM of 398600.52 + or - 0.12 cu km/sq sec was estimated using the GEM 10 gravity model, based on speed of light of 299792.458 km/sec. Tracking station coordinates were simultaneously adjusted, leaving geopotential model error as the dominant error source. Baselines between the adjusted NASA laser sites show better than 15 cm agreement with multiple short arc GEOS 3 solutions.

Martin, C. F.↗
NASA NTRS1973

Guiding of a balloon-borne coronagraph

Discussion of the guidance system of a balloon-borne coronagraph designed for IR solar corona observations at an altitude of 36 km. The coronagraph has two telescopes, measures 6 m in length, weighs 73 kg, is stabilized by a three-axis system, and has a pointing precision of plus or minus 10 sec of arc. Three torque motors are used to activate the tracking servo system of the coronagraph. An integral control system is used to prevent a permanent instrumental error. The coronagraph has been tested in two balloon missions.

Li, T.-C.↗
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↗
NASA NTRS1991

Ionospheric refraction effects on TOPEX orbit determination accuracy using the Tracking and Data Relay Satellite System (TDRSS)

This investigation concerns the effects on Ocean Topography Experiment (TOPEX) spacecraft operational orbit determination of ionospheric refraction error affecting tracking measurements from the Tracking and Data Relay Satellite System (TDRSS). Although tracking error from this source is mitigated by the high frequencies (K-band) used for the space-to-ground links and by the high altitudes for the space-to-space links, these effects are of concern for the relatively high-altitude (1334 kilometers) TOPEX mission. This concern is due to the accuracy required for operational orbit-determination by the Goddard Space Flight Center (GSFC) and to the expectation that solar activity will still be relatively high at TOPEX launch in mid-1992. The ionospheric refraction error on S-band space-to-space links was calculated by a prototype observation-correction algorithm using the Bent model of ionosphere electron densities implemented in the context of the Goddard Trajectory Determination System (GTDS). Orbit determination error was evaluated by comparing parallel TOPEX orbit solutions, applying and omitting the correction, using the same simulated TDRSS tracking observations. The tracking scenarios simulated those planned for the observation phase of the TOPEX mission, with a preponderance of one-way return-link Doppler measurements. The results of the analysis showed most TOPEX operational accuracy requirements to be little affected by space-to-space ionospheric error. The determination of along-track velocity changes after ground-track adjustment maneuvers, however, is significantly affected when compared with the stringent 0.1-millimeter-per-second accuracy requirements, assuming uncoupled premaneuver and postmaneuver orbit determination. Space-to-space ionospheric refraction on the 24-hour postmaneuver arc alone causes 0.2 millimeter-per-second errors in along-track delta-v determination using uncoupled solutions. Coupling the premaneuver and postmaneuver solutions, however, appears likely to reduce this figure substantially. Plans and recommendations for response to these findings are presented.

Radomski, M. S.↗
NASA NTRS1977

Improvements in navigation resulting from the use of dual spacecraft radiometric data

When two interplanetary spacecraft lie along similar geocentric lines-of-sight, navigational advantages may be achieved by navigating one spacecraft with respect to the other. Opportunities to employ this technique will become more common as more multiprobe missions are launched. The two Viking spacecraft and the two Mariner Jupiter/Saturn '77 spacecraft will be within two and three degrees of each other, respectively, for a large portion of their missions. Results of simulated analysis as well as the processing of real tracking data from the two Viking spacecraft reveal the following advantages of the dual spacecraft navigation technique: (1) cancellation of platform parameter and transmission media modeling errors in short arc solutions, (2) accurate encounter guidance for the trailing spacecraft, (3) reduction of total tracking requirements, and (4) rapid determination of the orbit following a maneuver on either spacecraft.

Chao, C. C.↗