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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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Surface Modeling to Support Small-Body Spacecraft Exploration and Proximity Operations

In order to simulate physically plausible surfaces that represent geologically evolved surfaces, demonstrating demanding surface-relative guidance navigation and control (GN&C) actions, such surfaces must be made to mimic the geological processes themselves. A report describes how, using software and algorithms to model body surfaces as a series of digital terrain maps, a series of processes was put in place that evolve the surface from some assumed nominal starting condition. The physical processes modeled in this algorithmic technique include fractal regolith substrate texturing, fractally textured rocks (of empirically derived size and distribution power laws), cratering, and regolith migration under potential energy gradient. Starting with a global model that may be determined observationally or created ad hoc, the surface evolution is begun. First, material of some assumed strength is layered on the global model in a fractally random pattern. Then, rocks are distributed according to power laws measured on the Moon. Cratering then takes place in a temporal fashion, including modeling of ejecta blankets and taking into account the gravity of the object (which determines how much of the ejecta blanket falls back to the surface), and causing the observed phenomena of older craters being progressively buried by the ejecta of earlier impacts. Finally, regolith migration occurs which stratifies finer materials from coarser, as the fine material progressively migrates to regions of lower potential energy.

Riedel, Joseph E.↗

PDS Archive Release of Apollo 11, Apollo 12, and Apollo 17 Lunar Rock Sample Images

Scientists at the Johnson Space Center (JSC) Lunar Sample Laboratory, Information Resources Directorate, and Image Science & Analysis Laboratory have been working to digitize (scan) the original film negatives of Apollo Lunar Rock Sample photographs [1, 2]. The rock samples, and associated regolith and lunar core samples, were obtained during the Apollo 11, 12, 14, 15, 16 and 17 missions. The images allow scientists to view the individual rock samples in their original or subdivided state prior to requesting physical samples for their research. In cases where access to the actual physical samples is not practical, the images provide an alternate mechanism for study of the subject samples. As the negatives are being scanned, they have been formatted and documented for permanent archive in the NASA Planetary Data System (PDS). The Astromaterials Research and Exploration Science Directorate (which includes the Lunar Sample Laboratory and Image Science & Analysis Laboratory) at JSC is working collaboratively with the Imaging Node of the PDS on the archiving of these valuable data. The PDS Imaging Node is now pleased to announce the release of the image archives for Apollo missions 11, 12, and 17.

Garcia, P. A.↗

Development of a Digital Meteorite Identification Program at University of New Mexico (UNM) (Institute of Meteoritics) and Southwestern Indian Polytechnic Institute (SIPI)

Determining the origin and chemical composition of suspect extra terrestrial specimens has lead to meteorite identification research programs. Such programs, like the University of New Mexico-Southwestern Indian Polytechnic Institute partnership, are being inundated with many non-meteorites (meteor wrongs) sent in by interested individuals from all over the world. This meteorite identification program developed a spreadsheet that aids in identifying the types of minerals in a sample for physical properties, possible meteorite characteristics, minerals and rock properties, and possible man made characteristics. Samples that show meteorite distinctiveness are further analyzed via the Scanning Electron Microprobe (SEM).

Gakin, R.↗

New theoretical models and ratio imaging techniques associated with the NASA earth resources spectral information system

Four independent investigations are reported; in general these are concerned with improving and utilizing the correlation between the physical properties of natural materials as evidenced in laboratory spectra and spectral data collected by multispectral scanners. In one investigation, two theoretical models were devised that permit the calculation of spectral emittance spectra for rock and mineral surfaces of various particle sizes. The simpler of the two models can be used to qualitatively predict the effect of texture on the spectral emittance of rocks and minerals; it is also potentially useful as an aid in predicting the identification of natural atmospheric aerosol constituents. The second investigation determined, via an infrared ratio imaging technique, the best pair of infrared filters for silicate rock-type discrimination. In a third investigation, laboratory spectra of natural materials were compressed into 11-digit ratio codes for use in feature selection, in searches for false alarm candidates, and eventually for use as training sets in completely automatic data processors. In the fourth investigation, general outlines of a ratio preprocessor and an automatic recognition map processor are developed for on-board data processing in the space shuttle era.

Vincent, R. K.↗

Planetary Rover Simulation for Lunar Exploration Missions

When planning planetary rover missions it is useful to develop intuition and skills driving in, quite literally, alien environments before incurring the cost of reaching said locales. Simulators make it possible to operate in environments that have the physical characteristics of target locations without the expense and overhead of extensive physical tests. To that end, NASA Ames and Open Robotics collaborated on a Lunar rover driving simulator based on the open source Gazebo simulation platform and leveraging ROS (Robotic Operating System) components. The simulator was integrated with research and mission software for rover driving, system monitoring, and science instrument simulation to constitute an end-to-end Lunar mission simulation capability. Although we expect our simulator to be applicable to arbitrary Lunar regions, we designed to a reference mission of prospecting in polar regions. The harsh lighting and low illumination angles at the Lunar poles combine with the unique reflectance properties of Lunar regolith to present a challenging visual environment for both human and computer perception. Our simulator placed an emphasis on high fidelity visual simulation in order to produce synthetic imagery suitable for evaluating human rover drivers with navigation tasks, as well as providing test data for computer vision software development.In this paper, we describe the software used to construct the simulated Lunar environment and the components of the driving simulation. Our synthetic terrain generation software artificially increases the resolution of Lunar digital elevation maps by fractal synthesis and inserts craters and rocks based on Lunar size-frequency distribution models. We describe the necessary enhancements to import large scale, high resolution terrains into Gazebo, as well as our approach to modeling the visual environment of the Lunar surface. An overview of the mission software system is provided, along with how ROS was used to emulate flight software components that had not been developed yet. Finally, we discuss the effect of using the high-fidelity synthetic Lunar images for visual odometry. We also characterize the wheel slip model, and find some inconsistencies in the produced wheel slip behaviour.

Allan, Mark↗

Eruption Characterisitics of Lunar Localized Pyroclastic Deposits Based on Water Content, Mineralogy, Regolith Properties

Lunar pyroclastic deposits are low albedo deposits present throughout the lunar surface including both maria and highlands, nearside and farside, and high latitudes and equatorial regions [e.g., 1]. There are two main types of pyroclastic deposits based upon size: localized (<2500 km2) and regional pyroclastic deposits (>2500 km2) [1]. In this study, we focus on the smaller localized pyroclastic deposits. The first and most detailed study of localized pyroclastic deposits are the deposits in Alphonsus crater [2]. This study included examining the radar and volumetric properties of these deposits. They found that vulcanian-like eruptions are most consistent with their observations. They imagined that a dike intrudes into the crust and creates a cooled basaltic cap. With increasing pressure under the basaltic cap, the pressure eventually overwhelms the surrounding rocks and results in explosive decompression. Later studies examined several pyroclastic deposits across the lunar surface using radar [2–4], digital terrain models (DTMs) [2,4], visible and near-infrared spectrometers [1,4–7], and a radiometer [4] to determine the mineralogy (e.g., olivine, glass, clinopyroxene, orthopyroxene, and plagioclase), deposit thickness and volume, proportion of juvenile material, radar backscatter, surface rock abundance, and regolith density. These various properties were examined against one another to better group localized pyroclastic deposits and understand how they relate to one another [4]. One of the studies divided pyroclastic deposits into three groups based upon the shape of the 1-μm absorption feature. The three Groups could be interpretated as having highlands and pyroclastic material (Group I), mare and pyroclastic material (Group II), and glass and orthopyroxene material (Group III) [5]. Building on that foundation, another study divided the pyroclastic deposits into four groups based upon their surface rock abundance and glass abundance [4], where deposits are classified based upon low surface rock abundance and high glass abundance (Glassy deposits); high rock abundance, high glass abundance (Blocky deposits); moderate rock abundance and low glass abundance (Crystalline deposits); and high rock abundance and low glass abundance (Indistinct Group). In addition to these studies, we can now look at their water contents using the new Effective Single Particle Absorption Thickness (ESPAT) parameter map [8]. The ESPAT map measures the strength of the 3-μm absorption feature in single scattering albedo space. The strength of this feature and its relationship to water content has been calibrated to returned samples, which allows for water content to be derived from remote spectral data (i.e., Moon Mineralogy Mapper). The goal of this study is to examine how water content in the pyroclastic deposits varies with respect to the geometric, mineralogic, and physical properties of the localized pyroclastic deposits as measured by a previous study [i.e., 4]. A more complete version of this work is found in [9].

D Trang↗