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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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SpaceCube is a family of Field Programmable Gate Array (FPGA) based on-board science-data processing systems developed at NASA Goddard Space Flight Center. This presentation provides an overview to the Future In-Space Operations Telecon Working Group.
We describe analytic methods for the design of the discrete elements of ultralight lattice structures. This modular building block strategy allows for relatively simple element manufacturing as well as relatively simple robotic assembly of low mass-density structures on orbit, with potential for disassembly and reassembly into highly varying and large structures. This method also results in a structure that is easily navigable by relatively small, mobile robots. The geometry of the cell can allow for high packing efficiency to minimize wasted payload volume while maximizing structural performance and constructability. We describe the effect of geometry choices on the mechanical properties and automated robotic constructability of a final system. Geometric properties considered include number of attachments per voxel, number of attachments per coefficient of volume, and effects of vertex, edge, and face connectivity of the unit cell. Mechanical properties considered include strength scaling, modulus scaling, and packing efficiency of the lattice. Automated constructibility metrics include volume allowance for an end-effector, strut clearance angle for an end-effector, and packing efficiency. These metrics were applied to six lattice unit cell geometries: cube, cuboctahedron, octahedron, octet, rhombic dodecahedron, and truncated octahedron. A case study is presented to determine the most suitable lattice system for a specific set of strength and modulus scaling requirements while optimizing for ease of robotic assembly.
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NASA originally developed the Compact Scanning Sub-millimeter Imaging Radiometer (CoSSIR) in the early 2000s, where it was own in the CRYSTAL-FACE and TC4 field experiments. With renewed interest in sub-millimeter remote sensing motivated by the upcoming launch of the EUMETSAT-sponsored Ice Cloud Imager (ICI), along with its potential for inclusion as part of the NASA Decadal Survey Aerosols, CLouds, Convection, and Precipitation (ACCP) observing architecture, CoSSIR is being refurbished to prepare for routine suborbital submillimeter measurements this decade. Upgrades include hardware (procurement of shared parts so that CoSMIR and CoSSIR can fly concurrently) and a change to the channels, with a focus on differential scattering and polarization at eight frequencies ranging from 170 to 640 GHz. As part of the refurbishment, the instrument name has been changed to Configurable Scanning Sub-millimeter Instrument/Radiometer, retaining the same acronym, in order to emphasize the science objectives that are enabled by CoSSIR's unique scanning geometry and channel set. These anticipated capabilities include two-dimensional pro ling of clouds and precipitation and characterization of the geometry (shape and orientation) of large cloud and precipitating ice particles.
Review of Assemblers low TRL architecture for in-space assemble tasks.
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A review of 1980s to early 2010s research supporting and documenting successful missions for on-orbit assembly of large space structures was conducted to bring together flexible space structure dynamics challenges and progress toward solutions. Research in this period focused on issues ranging from ground validation via unique test beds to in-space modal characterization and model adjustment. The phased assembly of the International Space Station (ISS), along with its evolving structural loads and dynamics, provides the central example, with ground and on-orbit tests of other systems providing contributing and contrasting examples of potential value for researchers facing today’s challenges.
This paper presents a method to intelligently adapt the baseline of a synthetic aperture radar based on Deep Rein- forcement Learning to help create plans for missions that use formation flight for Earth observation purposes. The main contribution of this paper is the initial results we have found from applying the tool to a toy mission: measuring the ver- tical structure of forests by using a synthetic aperture radar mounted on a formation of 7 satellites orbiting the Earth in a Sun Synchronous Orbit. We have found that with a reward function based on expected science return over time and fuel usage, the Deep Reinforcement Learning planner is able to create plans with positive scientific returns while minimizing fuel usage. We also find that fuel usage and collision avoid- ance planning is better done with traditional methods, as Deep Reinforcement Learning does not converge to optimal solutions.
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This is a summary presentation of postdoc research conducted as part of the Langley PROWESS project.
To meet the needs of future deep space exploration, NASA is interested in large-scale hardware systems in the agency’s thrust areas of solar power, communications, habitats and science interests. Scalable in-space assembly of physical systems is critical to massless exploration and in-space reliance goals. The ARMADAS project demonstrates the autonomous assembly of digital materials and structures. This provides automation technologies with potential for meeting long duration and deep space infrastructure needs, such as construction and maintenance of long duration spaceport, surface infrastructure, and habitat scale systems. Project demonstrations to date include a system that can fit into a small satellite-sized payload, which automatically assembles into primary structures, such as a small habitat module or array/antenna, using onboard robotic assemblers.
Envisioned capabilities from ARMADAS for lunar surface infrastructure construction
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Sleep inertia is the brief period of impaired alertness and performance experienced immediately after waking. Little is known about the neural mechanisms underlying this phenomenon. A better understanding of the neural processes during sleep inertia may offer insight into the awakening process. We observed brain activity every 15 min for 1 hr following abrupt awakening from slow wave sleep during the biological night. Using 32-channel electroencephalography, a network science approach, and a within-subject design, we evaluated power, clustering coefficient, and path length across frequency bands under both a control and a polychromatic short-wavelength-enriched light intervention condition. We found that under control conditions, the awakening brain is typified by an immediate reduction in global theta, alpha, and beta power. Simultaneously, we observed a decrease in the clustering coefficient and an increase in path length within the delta band. Exposure to light immediately after awakening ameliorated changes in clustering. Our results suggest that long-range network communication within the brain is crucial to the awakening process and that the brain may prioritize these long-range connections during this transitional state. Our study highlights a novel neurophysiological signature of the awakening brain and provides a potential mechanism by which light improves performance after waking.
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