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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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6 records
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NASA NTRS2013

Internal Characteristics of Phobos and Deimos from Spectral Properties and Density: Relationship to Landforms and Comparison with Asteroids

Compositional interpretations of new spectral measurements of Phobos and Deimos from Mars Express/OMEGA and MRO/CRISM and density measurements from encounters by multiple spacecraft support refined estimates of the moons' porosity and internal structure. Phobos' estimated macroporosity of 12-20% is consistent with a fractured but coherent interior; Deimos' estimated macroporosity of 23-44% is more consistent with a loosely consolidated interior. These internal differences are reflected in differences in surface morphology: Phobos exhibits a globally coherent pattern of grooves, whereas Deimos has a surface dominated instead by fragmental debris. Comparison with other asteroids .110 km in diameter shows that this correspondence between landforms and inferred internal structure is part of a pervasive pattern: asteroids interpreted to have coherent interiors exhibit pervasive, organized ridge or groove systems, whereas loosely consolidated asteroids have landforms dominated by fragmental debris and/or retain craters >1.3 body radii in diameter suggesting a porous, compressible interior.

Murchie, S. L.↗
NASA NTRS1976

The surface structure and composition of 60017,43

A surface fragment of 60017, itself an exposed section of Shadow Rock, has been characterized structurally and chemically using gas adsorption, helium pycnometry, scanning electron microscopy, and Auger and ESCA spectroscopy. The fragment is seen as a partially melted devitrified-glass impact breccia possessing a relatively low surface area, macroporosity, and low absolute density. The fragment appears to be plagioclase-enriched with respect to 60017 and to possess significant surface heterogeneity. Confirmation of the surface reduction of iron was obtained; however, the extent of this reduction varied from point to point. Significant amounts of surface volatiles were also found, and it is hypothesized that these were released during the North Ray cratering event. If this is proved correct, similar volatile concentrations should be found on other North Ray ejecta.

Cadenhead, D. A.↗
NASA NTRS1979

Variations in pore structure of reaction-bonded silicon nitride /RBSN/

A discussion is presented relating the observed pore structures (sizes) to the reaction mechanisms in reaction-bonded silicon nitride (alpha- and beta-Si3N4) on the basis of information available from the literature. While the techniques for reducing the residual macroporosity are quite well-developed for reaction-bonded Si3N4 (RBSN), it is important to be aware of three other orders of magnitude for porosity present in RBSN as a result of the nitriding process itself, and how these types of nitridation-induced porosity can be controlled. For ease of description, these types of nitridation-induced porosity are called micropores, nanopores, and picopores in order of their decreasing size. A scanning electron micrograph is presented, showing nanopores isolated in the unreacted Si and picopores in the alpha-matte Si3N4. The assumption that an alpha-matte growth mechanism is active explains the occurrence of nanopores and their partial filling with alpha-Si3N4, leaving behind very fine-grained alpha-matte and picopores.

Danforth, S. C.↗
NASA NTRS2001

The Porosity of 433 Eros

Data from the NEAR mission show the bulk density of 433 Eros is 2.67 g/cm 3 . Given an L or LL composition, the bulk porosity of Eros is in the range of 25-29% and the macroporosity is 14-18%. This is consistent with a fractured, but coherent asteroid. Additional information is contained in the original extended abstract.

Britt, D. T.↗
NASA NTRSDate not supplied

Consequences of Asteroid Characterization on the State of Knowledge about Inferred Physical Properties and Impact Risk

Physical characteristics of Near-Earth Objects (NEOs) are essential inputs to planetary defense assessments. The size, density, and strength of an NEO are critical inputs to modeling behavior during atmospheric entry as well as assessing the risk of impact. Similarly, knowledge of the physical characteristics of an object are necessary to evaluate the probable result of a mitigation mission. Usually, these attributes cannot be directly measured, but increasingly sophisticated methods have been developed to infer physical properties from related measurements of asteroids, meteors, and/or meteorites. Fortuitously, some of these measurements have been obtained for enough NEOs to elucidate the distribution of values across the sampled population. However, the situation becomes more challenging when considering a specific asteroid, since it is unlikely that all the relevant measurements have been made for any given object. We have developed a Bayesian network that can combine available information about a particular NEO with knowledge of the larger population to infer probabilistic values and uncertainties for physical characteristics of interest. Distributions of asteroid population albedos, taxonomic classes, and macroporosities, along with meteorite density distributions and associations between taxonomic classes and meteorite classes, provide the default distributions for the network’s parameter nodes. The inference network links parameters for each virtual asteroid either deterministically or probabilistically as appropriate, and eliminates any unphysical combinations of parameters. Within the context of planetary defense, our Bayesian network can be used to constrain the ranges of likely impactor properties, which can subsequently reduce the uncertainty in modelling of atmospheric entry, mitigation efficacy, and impact risk assessment. When additional measurements become available for a specific object, the network incorporates those measurements to generate virtual asteroids with property distributions that are consistent with the measurements. We will use the 2023 PDC scenario to demonstrate how the inference network can be combined with plausible characterization measurements to refine the state of knowledge about likely combinations of physical parameters and the resulting impact risk.

risk assessment↗
NASA NTRSDate not supplied

Consequences of Asteroid Characterization on the State of Knowledge about Inferred Physical Properties and Impact Risk

Physical characteristics of Near-Earth Objects (NEOs) are essential inputs to planetary defense assessments. The size, density, and strength of an NEO are critical inputs to modeling behavior during atmospheric entry as well as assessing the risk of impact. Similarly, knowledge of the physical characteristics of an object are necessary to evaluate the probable result of a mitigation mission. Usually, these attributes cannot be directly measured, but increasingly sophisticated methods have been developed to infer physical properties from related measurements of asteroids, meteors, and/or meteorites. Fortuitously, some of these measurements have been obtained for enough NEOs to elucidate the distribution of values across the sampled population. However, the situation becomes more challenging when considering a specific asteroid, since it is unlikely that all the relevant measurements have been made for any given object. We have developed a Bayesian network that can combine available information about a particular NEO with knowledge of the larger population to infer probabilistic values and uncertainties for physical characteristics of interest. Distributions of asteroid population albedos, taxonomic classes, and macroporosities, along with meteorite density distributions and associations between taxonomic classes and meteorite classes, provide the default distributions for the network’s parameter nodes. The inference network links parameters for each virtual asteroid either deterministically or probabilistically as appropriate, and eliminates any unphysical combinations of parameters. Within the context of planetary defense, our Bayesian network can be used to constrain the ranges of likely impactor properties, which can subsequently reduce the uncertainty in modelling of atmospheric entry, mitigation efficacy, and impact risk assessment. When additional measurements become available for a specific object, the network incorporates those measurements to generate virtual asteroids with property distributions that are consistent with the measurements. We will use the 2023 PDC scenario to demonstrate how the inference network can be combined with plausible characterization measurements to refine the state of knowledge about likely combinations of physical parameters and the resulting impact risk.

risk assessment↗