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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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239 records · Page 14
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NASA NTRS2007

Science in Exploration: From the Moon to Mars and Back Home to Earth

NASA is embarking on a grand journey of exploration that naturally integrates the past successes of the Apollo missions to the Moon, as well as robotic science missions to Mars, to Planet Earth, and to the broader Universe. The US Vision for Space Exporation (VSE) boldly lays out a plan for human and robotic reconnaissance of the accessible Universe, starting with the surface of the Moon, and later embracing the surface of Mars. Sustained human and robotic access to the Moon and Mars will enable a new era of scientific investigation of our planetary neighbors, tied to driving scientific questions that pertain to the evolution and destiny of our home planet, but which also can be related to the search habitable worlds across the nearby Universe. The Apollo missions provide a vital legacy for what can be learned from the Moon, and NASA is now poised to recapture the lunar frontier starting with the flight of the Lunar Reconnaissance Orbiter (LRO) in late 2008. LRO will provide a new scientific context from which joint human and robotic exploration will ensue, guided by objectives some of which are focused on the grandest scientific challenges imaginable : Where did we come from? Are we alone? and Where are we going? The Moon will serve as an essential stepping stone for sustained human access and exploration of deep space and as a training ground while robotic missions with ever increasing complexity probe the wonders of Mars. As we speak, an armada of spacecraft are actively investigating the red planet both from orbit (NASA's Mars Reconnaissance Orbiter and Mars Odyssey Orbiter, plus ESA's Mars Express) and from the surface (NASA's twin Mars Exploration Rovers, and in 2008 NASA's Phoenix polar lander). The dramatically changing views of Mars as a potentially habitable world, with its own flavor of global climate change and unique climate records, provides a new vantage point from which to observe and question the workings of our own planet Earth. By 2010 NASA will have its first mobile analytical laboratory operating on the surface of Mars (Mars Science Laboratory) in search of potentially subtle expressions of past life or at least of life-hospitable environments. Meanwhile back here on Planet Earth, NASA will be continuing to implement an increasingly comprehensive program of robotic missions that address major issues associated with global climate variability, and the "state variables" that affect the quality of human life on our home planet. Ultimately, the fmits of NASA's emergent program of Exploration (VSE) will provide never-beforepossible opportunities for scientific leadership and advancement, culminating in a new state of awareness from which to better plan for the sustainability of life on Earth and for extending Earth life to the Moon and eventually to Mars. As NASA nears its 50th anniversary, the unimaginable and unexpected wealth of strategic knowledge its missions have generated about Earth, the Universe, and our local Solar System boggles the mind and serves as a legacy of knowledge for Educators to inspire future generations.

Garvin, James B.↗
NASA NTRS2015

OSIRIS-REx, Returning the Asteroid Sample

This paper addresses the technical aspects of the sample return system for the upcoming Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer (OSIRIS-REx) asteroid sample return mission. The overall mission design and current implementation are presented as an overview to establish a context for the technical description of the reentry and landing segment of the mission.The prime objective of the OSIRIS-REx mission is to sample a primitive, carbonaceous asteroid and to return that sample to Earth in pristine condition for detailed laboratory analysis. Targeting the near-Earth asteroid Bennu, the mission launches in September 2016 with an Earth reentry date of September 24, 2023.OSIRIS-REx will thoroughly characterize asteroid Bennu providing knowledge of the nature of near-Earth asteroids that is fundamental to understanding planet formation and the origin of life. The return to Earth of pristine samples with known geologic context will enable precise analyses that cannot be duplicated by spacecraft-based instruments, revolutionizing our understanding of the early Solar System. Bennu is both the most accessible carbonaceous asteroid and one of the most potentially Earth-hazardous asteroids known. Study of Bennu addresses multiple NASA objectives to understand the origin of the Solar System and the origin of life and will provide a greater understanding of both the hazards and resources in near-Earth space, serving as a precursor to future human missions to asteroids.This paper focuses on the technical aspects of the Sample Return Capsule (SRC) design and concept of operations, including trajectory design and reentry retrieval. Highlights of the mission are included below.The OSIRIS-REx spacecraft provides the essential functions for an asteroid characterization and sample return mission: attitude control propulsion power thermal control telecommunications command and data handling structural support to ensure successful rendezvous with Bennu characterization of Bennus properties delivery of the sampler to the surface, and return of the spacecraft to the vicinity of the Earth sample collection, performed by the Touch-and-Go Sample Acquisition Mechanism (TAGSAM), to acquire a regolith sample from the surface Earth re-entry and SRC recovery. Following sample collection, OSIRIS-REx drifts away from Bennu until the Asteroid Departure Maneuver is commanded on March 4, 2021, sending OSIRIS-REx on a ballistic return cruise to Earth. No additional large deterministic maneuvers are required to return the SRC to Earth. During the cruise, tracking and trajectory correction maneuvers (TCMs) are performed as necessary to precisely target the entry corridor. As OSIRIS-REx approaches Earth, the reentry plans are reviewed starting about a year before arrival, and preparations begin. The spacecraft is targeted away from the Earth until 7 days before entry. The final two trajectory correction maneuvers bring the spacecraft on target toward the Utah Test and Training Range (UTTR), with sufficient time for contingency resolution. The SRC releases 4 hours prior to atmospheric entry interface and, using the Stardust capsule heritage design, employs a traditional drogue and main parachute descent system for a soft touchdown.

reentry↗
NASA NTRSDate not supplied

Nearest-Neighbor Machine Learning Feature Selection for Interpretation of Microbial Molecular Signatures from Isotope Ratio Mass Spectrometry Data

Mass spectrometry (MS) promises to be a powerful tool for potential biosignature detection during astrobiological missions on ocean worlds in our solar system. Accurate and generalizable machine learning methods could enhance science return on investment by predicting seawater chemistry and classifying isotopic biosignatures, either as a signature consistent with microbial life (biotic) or as a novelty (unclassified/unique). However, machine learning models are likely to be complex and involve interactions between MS features, making biosignatures difficult to interpret. Feature selection methods provide biological and chemical context that help interpret the mechanisms of machine learning models, but these methods also need the ability to detect complex interactions. Previously, we developed a machine learning feature selection algorithm called nearest-neighbor projected distance regression (NPDR) that has the ability to identify important model features that involve complex interactions and automatically reduce correlation and the dimensionality in a high-dimensional variable space. The standard distance metrics used in NPDR – Manhattan and Euclidean – assume the multivariate data are isotropic, which is often violated in real data due to differences in the covariance between variables. Thus, we extend NPDR to include a random forest distance, and other anisotropic distance metrics, for computing nearest neighbors. We also augment the isotope-ratio MS data with time-series features from the raw MS signal to improve biotic classification. We test NPDR on our novel experimental ocean world seawater analog MS data. We measure isotope fractionations of volatile CO 2 that could be measured in exospheres or plumes. Samples include baseline abiotic conditions using a range of possible seawater chemistry consistent with Europa and Enceladus, and biotic samples that include microbes in these seawaters. We use penalized NPDR with random forest proximity to identify interpretable microbial molecular signatures. We compare features with random forest importance, and we train a classifier that discriminates between biotic and abiotic samples with high accuracy. These ML-trained ocean-world analog MS data could be used to assist in identifying biosignatures during future missions.

geochemistry↗
NASA NTRSDate not supplied

Machine Learning Algorithms for Aerosol and Cloud Detection Using CATS on the ISS

Clouds and aerosols are one of the largest uncertainties in understanding and forecasting the Earth’s changing climate system. The type and height of aerosols are important factors in determining the top-of-atmosphere (TOA) radiation budget, either direct reflection of solar radiation back to space and/or absorption of solar radiation. In addition to their impact on the Earth’s climate system, aerosols near the surface from wildfires, man-made pollution events, and dust storms are hazardous to human health. The phase and height of clouds also play a critical role in determining the role of clouds in the Earth’s climate system. Cirrus clouds in the upper troposphere can induce a significant daytime TOA warming effect, while liquid water clouds near the surface cause a large corresponding cooling effect. Lidar measurements provide accurate vertically resolved information about clouds and aerosols, including complex multi-layer scenes where passive sensors are challenged and at night, when passive sensors are unable to measure cloud and aerosol properties. The Cloud-Aerosol Transport System (CATS) is a lidar instrument that operated for 33 months on the International Space Station (ISS) at the 1064 nm wavelength to measure attenuated total backscatter and depolarization ratio. These fundamental measurements are used to derive “vertical feature mask” cloud and aerosol products, including layer top/base heights, layer geometrical thickness, aerosol type, and cloud phase. While space-based lidar systems like CATS provide cloud and aerosol vertical distributions that improve our understanding of the climate system, averaging of the daytime data from these sensors is required, at the expense of spatial resolution, to improve the daytime signal-to noise (SNR) and thus atmospheric layer detection. This presentation shows results from machine learning (ML) techniques that, when applied to CATS data: 1. improve the 1064 nm SNR 2. enable detection of atmospheric features during daytime with a horizontal resolution of 350 m or 5 km (compared to the 60 km required for standard CATS data products) 3. increase the number of atmospheric layers detected in the CATS data. A Convolutional Neural Network (CNN) trained using CATS standard data products also demonstrated the potential for improved cloud-aerosol discrimination, cloud phase, and aerosol typing compared to the operational CATS algorithms for cloud edges and complex near-surface scenes during daytime. The ML tools described in this paper can facilitate the development of smaller, low-cost lidar systems in the future and enable real-time accessibility of lidar data products from future lidar systems for monitoring and forecasting of hazardous events.

John Yorks↗
NASA NTRS2017

The Saturn System Through the Eyes of Cassini

More than 400 years ago, Galileo Galilei trained his homemade telescope on the night sky and observed that Saturn had two objects closely related to the planet extending on either side. At the time, in 1610, Galileo declared them to be moons. A few decades later, Saturn moon science accelerated at a dizzying pace. Christiaan Huygens first observed Saturn's largest moon Titan in 1655 and was the first to describe the extended moon-like features at Saturn as a disk of material sounding the planet. From 1671 to 1674, Giovanni Cassini discovered the moons lapetus, Rhea, Dione and Tethys. In 1675, Cassini discovered the gap in Saturn's rings that we now know as the Cassini Division. In the space age, before the Cassini-Huygens mission, we had only hints of the discoveries awaiting us at Saturn. Pioneer 11 and Voyagers 1 and 2 conducted flybys decades ago. But these quick encounters didn't allow time for more extensive research. NASA and the European Space Agency created a partnership to orbit a Saturn orbiter (Cassini) and a lander (Huygens) on Titan. Like its namesakes, the Cassini-Huygens mission not only discovered previously unknown moons, but it also helped us understand the science behind their formation, their interactions with the rings, and how truly diverse they are. The Cassini-Huygens mission revolutionized what we know about the Saturn system. The rings of Saturn, the moons, and the planet itself offer irresistible and inexhaustible subjects for intense study, and Cassini-Huygens did not disappoint. The Saturnian system proved to be a rich ground for science exploration and discoveries, and Cassini has been nothing short of a discovery machine. At the time Cassini plunged into Saturn at the end of its mission, it had observed the planet for a little less than half of a Saturn year. But it also orbited the gas giant 293 times, forever changing our understanding of the Saturn system and yielding tremendous insight for understanding the entire Solar System.

Green, James↗