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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Atmospheric Mining in the Outer Solar System: Resource Capturing, Exploration, and Exploitation

Atmospheric mining in the outer solar system (AMOSS) has been investigated as a means of fuel production for high-energy propulsion and power. Fusion fuels such as helium 3 (He-3) and hydrogen can be wrested from the atmospheres of Uranus and Neptune and either returned to Earth or used in-situ for energy production. 3He and hydrogen (deuterium, etc.) were the primary gases of interest, with hydrogen being the primary propellant for nuclear thermal solid core and gas core rocket-based atmospheric flight. A series of analyses were undertaken to investigate resource capturing aspects of AMOSS. These analyses included the gas capturing rate, storage options, and different methods of direct use of the captured gases. Additional supporting analyses were conducted to illuminate vehicle sizing and orbital transportation issues. While capturing 3He, large amounts of hydrogen and helium 4 (He-4) are produced. With these two additional gases, the potential exists for fueling small and large fleets of additional exploration and exploitation vehicles. Additional aerospacecraft or other aerial vehicles (UAVs, balloons, rockets, etc.) could fly through the outer-planet atmosphere to investigate cloud formation dynamics, global weather, localized storms or other disturbances, wind speeds, the poles, and so forth. Deep-diving aircraft (built with the strength to withstand many atmospheres of pressure) powered by the excess hydrogen or 4He may be designed to probe the higher density regions of the gas giants.

Palaszewski, Bryan↗
NASA NTRS2017

NASA's Robotic Mining Competition Provides Undergraduates Full Life Cycle Systems Engineering Experience

NASA has held an annual robotic mining competition for teams of university/college students since 2010. This competition is yearlong, suitable for a senior university engineering capstone project. It encompasses the full project life cycle from ideation of a robot design, through tele-operation of the robot collecting regolith in simulated Mars conditions, to disposal of the robot systems after the competition. A major required element for this competition is a Systems Engineering Paper in which each team describes the systems engineering approaches used on their project. The score for the Systems Engineering Paper contributes 25% towards the team’s score for the competition’s grand prize. The required use of systems engineering on the project by this competition introduces the students to an intense practical application of systems engineering throughout a full project life cycle.

Stecklein, Jonette↗
NASA NTRS2018

Enriching the Twitter Stream Increasing Data Mining Yield and Quality Using Machine Learning

Social media data streams are important sources of real-time and historical global information for science applications. At the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), we are exploring the Twitter data stream for its potential in augmenting the validation program of NASA Earth science missions, specifically the Global Precipitation Measurement (GPM) mission. We have implemented a tweet processing infrastructure that outputs classified precipitation tweets. Inputs are "passive" tweets, along with a smaller number of tweets from "active" participants, i.e., those knowingly contributing to our effort. The "active" tweets, presumably of higher quality, enrich the Twitter stream. "Active" sources include data scraped from other social media (e.g., public Facebook posts) and data from existing crowdsourcing programs (e.g., mPING reports). In addition, there is likely relevant precipitation information in images and documents that are the end points of links often included in tweets. Information derived from these "active" sources could then be tweeted into the Twitter stream, thus enriching its quality. The objective of our current work is to mine these tweet­ linked images and documents, using neural networks, to increase the information content and quality related to precipitation. For images, we classified them as either precipitation-related or not. For training and validation, we used images obtained via the Google custom search API. We created two models: (1) by training a simple Convolutional Neural Network and (2) by using transfer learning principles to adapt a pre-trained object recognition model. For documents, both those linked to tweets and the tweet contents, we trained Hierarchical Attention Networks to determine precipitation occurrence, type, and intensity. For training and validation, we used a keyword-filtered tweet data set labelled with ground truth data from Dark Sky (an API to retrieve weather-related labels) and the National Severe Storms Laboratory's Multi­ Radar/Multi-Sensor (MRMS) system. Our results demonstrated the efficacy of our machine learning approaches for enriching the Twitter stream, to derive information potentially useful for validation of earth science satellite data.

Albayrak, Arif↗
NASA NTRS2021

Examining Runner’s Outdoor Heat Exposure Using Urban Microclimate Modeling and GPS Trajectory Mining

It is important to quantify human heat exposure in order to evaluate and mitigate the negative impacts of heat on human well-being in the context of global warming. This study proposed a human-centric framework to examine human personal heat exposure based on anonymous GPS trajectories data mining and urban microclimate modeling. The mean radiant temperature (Tmrt) that represents the human body’s energy balance was used to indicate human heat exposure. The meteorological data and high-resolution 3D urban model generated from multispectral remotely sensed images and LiDAR data were used as inputs in urban microclimate modeling to map the spatio-temporal distribution of the Tmrt in the Boston metropolitan area. The anonymous human GPS trajectory data collected from fitness Apps was used to map the spatiotemporal distribution of human outdoor activities. By overlaying the anonymous GPS trajectories on the generated spatio-temporal maps of Tmrt, this study further examined the heat exposure of runners in different age-gender groups in the Boston area. Results show that there is no significant difference in terms of heat exposure for female and male runners. The female runners in the age of 45-54 are exposed to more heat than female runners of 18-24 and 25-34, while there is no significant difference among male runners. This study proposed a novel method to estimate human heat exposure, which would shed new light on mitigating the negative impacts of heat on human health.

Personal heat exposure↗
NASA NTRS2022

FY22 X-Hab: Colorado School of Mines: Planetary Resource & In-Situ Material Habitat Outfitting for Space Exploration (PRISM-HOUSE)

The Colorado School of Mines (CSM) was awarded a project under the Moon to Mars eXploration Systems and Habitation (M2M X-Hab) Academic Innovation Challenge on June 10, 2021 for the proposed Planetary Resource & In-Situ Material Habitat Outfitting for Space Exploration (PRISM-HOUSE) lunar habitat system. The project deliverables have been executed concurrently with coursework in the CSM Space Resources program, specifically for the Space Resources Project I & II classes. A team was established in the fall semester of 2021 and a Systems Engineering process was followed to brainstorm initial concepts, determine objectives, flow down top-level requirements, and identify areas for which the project could best further the understanding of the overall system through targeted detailed design and design evaluation testing. This report summarizes the overall project, as well as specific work completed in the spring semester of 2022. PRISM-HOUSE is a lunar habitat system that is deployed on the lunar surface robotically and supports safe, long-term human occupancy while maximizing the use of in-situ resources and minimizing the mass of supplies and equipment that must be delivered from Earth. In this report, the team provides an overview and description of the objectives, the system, and how the current design was selected from various alternatives, as well as the resulting products of systems engineering tasks such as product and specification trees and flow-down requirements. A review of current risks and planned mitigation are reviewed. Four key PRISM-HOUSE systems are explored: the inflatable Habitat, External Structures & Environmental Protection (ESEP), Human Interior Goods (HIG), and ECLSS & Remote Outfitting (E&RO); for each, the team provides a detailed system description and an overview of analyses performed supporting final design, followed by a review of each Design Evaluation Test (DET) performed and resulting conclusions. The report closes with a suggestion of next steps.

Peter Corwin↗
NASA NTRSDate not supplied

Polar Resources Ice Mining Experiment-1 (PRIME-1) NASA’s First Polar Drilling and Volatiles Detection Mission

The US Administration announced in 2019 that NASA would return to the Moon where it would seek to establish a sustainable lunar presence. In Situ Resource Utilization (ISRU) is needed to sustain and grow hu-man surface exploration and it is therefore a vital part of ensuring this bold endeavor. ISRU requires ground-truth on physical, mineral, and volatile characteristics of the resources. Water, a key and game-changing resource, exists in the polar regions of the Moon. Learning to harvest and use this resource first requires understanding where the resource is abundantly located and on what scales. Harvested water, which is usable for life support and fuel, must be identified, quantified, and assessed for its mining feasibility. The project goal for PRIME-1 is to develop a flight-ready instrumentation package that can assess the volatiles at a polar lunar landing location. PRIME-1 is the combination of two instruments; Mass Spectrometer observing lunar operations (MSolo) and The Regolith and Ice Drill for Exploring New Terrain (TRIDENT). TRIDENT is an 1-meter augering drill capable of bringing incremental lunar regolith samples to the surface for volatile analysis. MSolo is a modified, commercial-off-the-shelf (COTS) mass spectrometer capable of qualifying and quantifying atomic species in the 1-100 amu range, including isotopic differentiation. These two lunar flight instruments operating together make up the PRIME-1 instrument suite. PRIME-1 intends to fly on and operate from a static lunar lander acquired by the NASA Commercial Lunar Payload Services (CLPS) acquisition process. The PRIME-1 payload suite was selected to fly on Intuitive Machines Nova-C lander, and is currently targeting a late Fall 2023 landing attempt.

MSolo↗
NASA NTRSDate not supplied

Polar Resources Ice Mining Experiment-1 (PRIME-1) NASA’s First Polar Drilling and Volatiles Detection Mission

The US Administration announced in 2019 that NASA would return to the Moon where it would seek to establish a sustainable lunar presence. In Situ Resource Utilization (ISRU) is needed to sustain and grow hu-man surface exploration and it is therefore a vital part of ensuring this bold endeavor. ISRU requires ground-truth on physical, mineral, and volatile characteristics of the resources. Water, a key and game-changing resource, exists in the polar regions of the Moon. Learning to harvest and use this resource first requires understanding where the resource is abundantly located and on what scales. Harvested water, which is usable for life support and fuel, must be identified, quantified, and assessed for its mining feasibility. The project goal for PRIME-1 is to develop a flight-ready instrumentation package that can assess the volatiles at a polar lunar landing location. PRIME-1 is the combination of two instruments; Mass Spectrometer observing lunar operations (MSolo) and The Regolith and Ice Drill for Exploring New Terrain (TRIDENT). TRIDENT is an 1-meter augering drill capable of bringing incremental lunar regolith samples to the surface for volatile analysis. MSolo is a modified, commercial-off-the-shelf (COTS) mass spectrometer capable of qualifying and quantifying atomic species in the 1-100 amu range, including isotopic differentiation. These two lunar flight instruments operating together make up the PRIME-1 instrument suite. PRIME-1 intends to fly on and operate from a static lunar lander acquired by the NASA Commercial Lunar Payload Services (CLPS) acquisition process. The PRIME-1 payload suite was selected to fly on Intuitive Machines Nova-C lander, and is currently targeting a late Fall 2023 landing attempt.

MSolo↗
NASA NTRSDate not supplied

Data Fusion and Mining Techniques to Map Water Use and Drought across Spatial and Temporal Scales

As the world’s water resources come under increasing tension due to dual stressors of climate change and population growth, accurate knowledge of water consumption through evapotranspiration (ET) over a range in spatial scales will be critical in developing adaptation strategies. Remote sensing methods for monitoring consumptive water use (e.g, ET) are becoming increasingly important, especially in areas of significant water and food insecurity. One method to estimate ET from satellite-based methods, the Atmosphere Land Exchange Inverse (ALEXI) model uses the change in mid-morning land surface temperature to estimate the partitioning of sensible and latent heat fluxes which are then used to estimate daily ET. This presentation will outline several recent enhancements to the ALEXI modeling system, with a focus on global ET and drought monitoring. Until recently, ALEXI has been limited to areas with high resolution temporal sampling of geostationary sensors. The use of geostationary sensors makes global mapping a complicated process, especially for real-time applications, as data from as many as five different sensors are required to be ingested and harmonized to create a global mosaic. However, our research team has developed a new and novel method of using twice-daily observations from polar-orbiting sensors such as MODIS and VIIRS to estimate the mid-morning rise in LST that is used to drive the energy balance estimations within ALEXI. This allows the method to be applied globally using a single sensor (in this case, initially MODIS with a planned transition to VIIRS) rather than a global compositing of all available geostationary data. Other advantages of this new method include the higher spatial resolution provided by MODIS and VIIRS and the increased sampling at high latitudes where oblique view angles limit the utility of geostationary sensors. This presentation will focus on global applications for mapping water use and drought using data mining and data fusion across spatial scales extending from 5-km to 30-m “field-scale” estimates.

Christopher Hain↗
DOE OSTI2024

Voltage Mining for (De)lithiation-Stabilized Cathodes and a Machine Learning Model for Li-Ion Cathode Voltage

Advances in lithium-metal anodes have inspired interest in discovery of Li-free cathodes, most of which are natively found in their charged state. This is in contrast to today's commercial lithium-ion battery cathodes, which are more stable in their discharged state. In this study, we combine calculated cathode voltage information from both categories of cathode materials, covering 5577 and 2423 total unique structure pairs, respectively. The resulting voltage distributions with respect to the redox pairs and anion types for both classes of compounds emphasize design principles for high-voltage cathodes, which favor later Period 4 transition metals in their higher oxidation states and more electronegative anions like fluorine or polyanion groups. Generally, cathodes that are found in their charged, delithiated state are shown to exhibit voltages lower than those that are most stable in their lithiated state, in agreement with thermodynamic expectations. Deviations from this trend are found to originate from different anion distributions between redox pairs. In addition, a machine learning model for voltage prediction based on chemical formulas is trained and shows state-of-the-art performance when compared to two established composition-based ML models for material properties predictions, Roost and CrabNet.

25 ENERGY STORAGE↗
DOE OSTI2026

Unsupervised atomic data mining via multi-kernel graph autoencoders for machine learning force fields

Constructing a chemically diverse dataset while avoiding sampling bias is critical to training efficient and generalizable force fields. However, in computational chemistry and materials science, many common dataset generation techniques are prone to oversampling regions of the potential energy surface. Furthermore, these regions can be difficult to identify and isolate from each other or may not align well with human intuition, making it challenging to systematically remove bias in the dataset. While traditional clustering and pruning (down-sampling) approaches can be useful for this, they can often lead to information loss or a failure to properly identify distinct regions of the potential energy surface due to difficulties associated with the high dimensionality of atomic descriptors. In this work, we introduce the Multi-kernel Edge Attention-based Graph Autoencoder (MEAGraph) model, an unsupervised approach for analyzing atomic datasets. MEAGraph combines multiple linear kernel transformations with attention-based message passing to capture geometric sensitivity and enable effective dataset pruning without relying on labels or extensive training. Demonstrated applications on niobium, tantalum, and iron datasets show that MEAGraph efficiently groups similar atomic environments, allowing for the use of basic pruning techniques for removing sampling bias. This approach provides an effective method for representation learning and clustering that can be used for data analysis, outlier detection, and dataset optimization.

Materials science↗
DOE OSTI2026

Data Mining of Groundwater to Identify MAGs with Methane, Propane and Toluene Monooxygenases

Whole genome sequencing datasets, involving more than 600 groundwater samples, from nine countries, were analyzed to identify metagenome assembled genomes (MAGs) containing full operons for propane monooxygenase, soluble methane monooxygease, toluene monooxygenase and particulate ammonia/methane monooxygenase. The enzymes encoded by these genes are a focus of interest because of their ability to degrade common groundwater contaminants. Due to the large amount of data, sequence analyses involved more than 80 individual KBase narratives. The approach followed the KBase tutorial called "Metagenome-Assembled Genome Extraction from a Compost Microbiome Enrichment" The generated MAGs were exported from each individual narrative into separate summary KBase narratives for each monooxygenase. Three KBase narratives were generated for particulate ammonia/methane monooxygenase, due to the large number of MAGs identified.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2002

Dibenzo-p-dioxins in the environment from ceramics and pottery produced from ball clay mined in the United States

Processed ball clay samples used in the production of ceramics and samples of the ceramic products were collected and analyzed for the presence and concentration of the 2,3,7,8-Cl substituted polychlorinated dibenzo-p-dioxins and -furans (PCDDs/PCDFs). The processed ball clay had average PCDD concentrations of 3.2 ng/g toxic equivalents, a congener profile, and isomer distribution consistent with those found previously in raw ball clay. The PCDF concentrations were below the average limit of detection (LOD) of 0.5 pg/g. The final fired ceramic products were found to be free of PCDDs/PCDFs at the LODs. A consideration of the conditions involved in the firing process suggests that the PCDDs, if not destroyed, may be released to the atmosphere and could represent an as yet unidentified source of dioxins to the environment. In addition, the PCDDs in clay dust generated during manufacturing operations may represent a potential occupational exposure.

Aluminum Silicates/chemistry↗