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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

The Lunar Regolith as a Remote Sensing Target for the Lunar Reconnaissance Orbiter (LRO)

Of the 6 instruments and one technology demonstration aboard the LRO, only CRaTER does not measure some kind of interaction of particles with the lunar regolith. LEND detects neutron fluence that contains information about the number density of protons in the upper regolith. To infer the presence of protons, the PI must assume a model that characterizes the surface as a collection of atoms. Thus, LEND does not sense the regolith as a structure. LROC, LOLA, and LAMP sense reflected photons whose wavelength is much shorter than the median particle size in the regolith. The photons interact with electrons, either in atomic shells or in chemical bonds. These interactions occur within a nanometer or so of the surface of a particle. Thus, the particles are macroscopic objects and models of the reflection process invoke ray-tracing optics. DIVINER senses photons that have been emitted by surface particles through thermal phonon processes. The wavelengths detected by the instrument are of the same order as the median particle size, and the photons contain information on particle dimensions as well as the molecular bonds in the constituent compounds. The Mini-RF synthetic aperture radar generates and detects photons of a few centimeters wavelength that interact with the regolith as a dielectric, the dielectric properties of the particulate component being described through effective medium theory. However, the interaction with rocks (macroscopic objects of interest to geologists) can be characterized using Fresnel or Mie models of electromagnetic properties.

Mendell, Wendell W.↗
NASA NTRS2009

Epithermal Neutron Observations and Lunar South Pole Targeting for LCROSS Impact Planning using the Lunar Reconnaissance Orbiter (LRO), Lunar Exploring Neutron Detector (LEND)

LCROSS impact targeting and planning efforts included quantifying South Polar epithermal neutron flux depressions in early LEND mapped results to maximize the expected plume Hydrogen (H) yield. Epithermal neutron surface fluxes are a key geochemical indicator of surface Hydrogen (H) concentration inferred to be elevated in polar permanent shadow regions (PSR). LCROSS impact target regions were delineated as (PSR) using illumination modeling of polar topography. To quantify targets potential yield for LCROSS, LEND epithermal neutron flux observations were integrated over LCROSS targets of interest and compared to background observations. Discussion will define methods review impact prior estimates and contrast post impact results.

McClanahan, T. P.↗
NASA NTRS2011

Lunar Reconnaissance Orbiter (LRO) Observations with the Lunar Exploration Neutron Detector (LEND): Neutron Suppression Regions (NSR) and Polar Hydrogen

Orbital detection of neutrons has become the dominant remote sensing technique for detecting and inferring H concentrations and its spatial distribution beneath planetary surfaces [Lawrence et al, (2010) Icarus, 205, pp. 195-209, Mitrofanov et al (2007) Science 297(5578), 78-81]. Indications for the presence of localized and relatively high water content was provided by LRO and LCROSS. LEND identified Cabeus, as the most promising LCROSS impact site [Mitrofanov I. et al. (2010) Science, 330, 483], and instruments onboard LRO and LCROSS have measured signatures of water, H2 and other volatiles in the impact plume [Colaprete A. et al. (2010) Science, 339,463, Gladstone R. et al. (2010) Science, 330, 472].

Chin, G.↗
NASA NTRS2012

DROP: Durable Reconnaissance and Observation Platform

Robots have been a valuable tool for providing a remote presence in areas that are either inaccessible or too dangerous for humans. Having a robot with a high degree of adaptability becomes crucial during such events. The adaptability that comes from high mobility and high durability greatly increases the potential uses of a robot in these situations, and therefore greatly increases its usefulness to humans. DROP is a lightweight robot that addresses these challenges with the capability to survive large impacts, carry a usable payload, and traverse a variety of surfaces, including climbing vertical surfaces like wood, stone, and concrete. The platform is crash-proof, allowing it to be deployed in ways including being dropped from an unmanned aerial vehicle or thrown from a large MSL-class (Mars Science Laboratory) rover.

Parness, Aaron↗
NASA NTRS2013

ULTRA: Underwater Localization for Transit and Reconnaissance Autonomy

This software addresses the issue of underwater localization of unmanned vehicles and the inherent drift in their onboard sensors. The software gives a 2 to 3 factor of improvement over the state-of-the-art underwater localization algorithms. The software determines the localization (position, heading) of an AUV (autonomous underwater vehicle) in environments where there is no GPS signal. It accomplishes this using only the commanded position, onboard gyros/accelerometers, and the bathymetry of the bottom provided by an onboard sonar system. The software does not rely on an onboard bathymetry dataset, but instead incrementally determines the position of the AUV while mapping the bottom. In order to enable long-distance underwater navigation by AUVs, a localization method called ULTRA uses registration of the bathymetry data products produced by the onboard forward-looking sonar system for hazard avoidance during a transit to derive the motion and pose of the AUV in order to correct the DR (dead reckoning) estimates. The registration algorithm uses iterative point matching (IPM) combined with surface interpolation of the Iterative Closest Point (ICP) algorithm. This method was used previously at JPL for onboard unmanned ground vehicle localization, and has been optimized for efficient computational and memory use.

Huntsberger, Terrance L.↗
NASA NTRS2014

Lunar Reconnaissance Orbiter Orbit Determination Accuracy Analysis

LRO definitive and predictive accuracy requirements were easily met in the nominal mission orbit, using the LP150Q lunar gravity model. center dot Accuracy of the LP150Q model is poorer in the extended mission elliptical orbit. center dot Later lunar gravity models, in particular GSFC-GRAIL-270, improve OD accuracy in the extended mission. center dot Implementation of a constrained plane when the orbit is within 45 degrees of the Earth-Moon line improves cross-track accuracy. center dot Prediction accuracy is still challenged during full-Sun periods due to coarse spacecraft area modeling - Implementation of a multi-plate area model with definitive attitude input can eliminate prediction violations. - The FDF is evaluating using analytic and predicted attitude modeling to improve full-Sun prediction accuracy. center dot Comparison of FDF ephemeris file to high-precision ephemeris files provides gross confirmation that overlap compares properly assess orbit accuracy.

Gravitation↗
NASA NTRS2014

Lunar Reconnaissance Orbiter Orbit Determination Accuracy Analysis

Results from operational OD produced by the NASA Goddard Flight Dynamics Facility for the LRO nominal and extended mission are presented. During the LRO nominal mission, when LRO flew in a low circular orbit, orbit determination requirements were met nearly 100% of the time. When the extended mission began, LRO returned to a more elliptical frozen orbit where gravity and other modeling errors caused numerous violations of mission accuracy requirements. Prediction accuracy is particularly challenged during periods when LRO is in full-Sun. A series of improvements to LRO orbit determination are presented, including implementation of new lunar gravity models, improved spacecraft solar radiation pressure modeling using a dynamic multi-plate area model, a shorter orbit determination arc length, and a constrained plane method for estimation. The analysis presented in this paper shows that updated lunar gravity models improved accuracy in the frozen orbit, and a multiplate dynamic area model improves prediction accuracy during full-Sun orbit periods. Implementation of a 36-hour tracking data arc and plane constraints during edge-on orbit geometry also provide benefits. A comparison of the operational solutions to precision orbit determination solutions shows agreement on a 100- to 250-meter level in definitive accuracy.

Gravitation↗
NASA NTRS2015

Orbit Determination for the Lunar Reconnaissance Orbiter Using an Extended Kalman Filter

Since launch, the FDF has performed daily OD for LRO using the Goddard Trajectory Determination System (GTDS). GTDS is a batch least-squares (BLS) estimator. The tracking data arc for OD is 36 hours. Current operational OD uses 200 x 200 lunar gravity, solid lunar tides, solar radiation pressure (SRP) using a spherical spacecraft area model, and point mass gravity for the Earth, Sun, and Jupiter. LRO tracking data consists of range and range-rate measurements from: Universal Space Network (USN) stations in Sweden, Germany, Australia, and Hawaii. A NASA antenna at White Sands, New Mexico (WS1S). NASA Deep Space Network (DSN) stations. DSN data was sparse and not included in this study. Tracking is predominantly (50) from WS1S. The OD accuracy requirements are: Definitive ephemeris accuracy of 500 meters total position root-mean-squared (RMS) and18 meters radial RMS. Predicted orbit accuracy less than 800 meters root sum squared (RSS) over an 84-hour prediction span.

LRO↗