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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

Low Cost Propulsion Technology Testing at the Stennis Space Center: Propulsion Test Article and the Horizontal Test Facility

The need for low cost access to space has initiated the development of low cost liquid rocket engine and propulsion system hardware at the Marshall Space Flight Center. This hardware will be tested at the Stennis Space Center's B-2 test stand. This stand has been reactivated for the testing of the Marshall designed Fastrac engine and the Propulsion Test Article. The RP-1 and LOX engine is a turbopump fed gas generator rocket with an ablative nozzle which has a thrust of 60,000 lbf. The Propulsion Test Article (PTA) is a test bed for low cost propulsion system hardware including a composite RP-I tank, flight feedlines and pressurization system, stacked in a booster configuration. The PTA is located near the center line of the B-2 test stand, firing vertically into the water cooled flame deflector. A new second position on the B-2 test stand has been designed and built for the horizontal testing of the Fastrac engine in direct support of the X-34 launch vehicle. The design and integration of these test facilities as well as the coordination which was required between the two Centers is described and lessons learned are provided. The construction of the horizontal test position is discussed in detail. The activation of these facilities is examined and the major test milestones are described.

Fisher, Mark F.↗
NASA NTRS2014

Spacecraft Hybrid Control At NASA: A Look Back, Current Initiatives, and Some Future Considerations

There is a heightened interest within NASA for the design, development, and flight implementation of mixed actuator hybrid attitude control systems for science spacecraft that have less than three functional reaction wheel actuators. This interest is driven by a number of recent reaction wheels failures on aging, but still scientifically productive, NASA spacecraft. This paper describes the highlights of the first NASA Cross-Center Hybrid Control Workshop that was held in Greenbelt, Maryland in April of 2013 under the sponsorship of the NASA Engineering and Safety Center (NESC). A brief historical summary of NASA's past experiences with spacecraft mixed actuator hybrid attitude control approaches, some of which were implemented on-orbit, will be provided. This paper will also convey some of the lessons learned and best practices captured at that workshop. Some relevant recent and current hybrid control activities will be described with an emphasis on work in support of a repurposed Kepler spacecraft. Specific technical areas for future considerations regarding spacecraft hybrid control will also be identified.

attitude control↗
NASA NTRS2019

NASA KSC Intern Final Report - Virtual Reality in STEM Engagement

For this internship, I was a part of the multi-center NextGen STEM (Science, Technology, Engineering, and Mathematics) pilot. Specifically, I worked with the Developing Commercial Crew Program (CCP) Capabilities team that focuses on human spaceflight to the space station with NASA’s commercial partners. Composed of NASA employees and contractors (who are connected to KSC, JSC, and LaRC), they work to ensure that students will experience traditional classroom content and be immersed in emerging technologies in order to explore Commercial Crew missions and launch facilities. This group is in the process of creating educational products and resources to inspire the next generation to pursue STEM and to allow educators to have access to unique and enticing STEM activities. These products include: age-appropriate classroom lessons (including activity sheets and challenges) for kindergarten through twelfth grade; a CCP app and corresponding guide; learning experiences for students as well as educators; and virtual field trips and tours using NASA-created Virtual Reality (VR) videos and a custom VR app. My particular assignment was managing the VR equipment, developing an understanding of how to maximize the use of the equipment, creating documents that explain and convey the operations and procedures concerning the equipment (including lessons learned from equipment use), and training others to utilize the equipment effectively and correctly for conference events and group demonstrations. Over the course of this internship, I helped my team outline and learn essential procedures of operation for the VR equipment, solved technology problems that arose (due to the nature of creating unique products), and encouraged audiences at KSC and various conferences to consider using the CCP VR videos in their classrooms as an opportunity to engage and challenge the next generation to pursue STEM careers.

STEM↗
NASA NTRS2022

Ten Years of VIIRS On-Orbit Geolocation Calibration and Performance

The first innovative Visible Infrared Imaging Radiometer Suite (VIIRS) sensor aboard the Suomi National Polar-orbiting Partnership (SNPP) satellite has been in operation for 10 years since its launch on 28 October 2011. The second VIIRS sensor aboard the first Join Polar Satellite System (JPSS-1) satellite has been in operation for 4 years since its launch on 18 November 2017, which became NOAA-20. Well-geolocated and radiometrically calibrated Level-1 sensor data records (SDRs) from VIIRS are crucial to numerical weather prediction (NWP) and Level-2+ environmental data record (EDR) algorithms and products. The high quality of Level-2+ EDRs is a requirement for the continuity of NASA Earth science data records (ESDRs) and climate data records (CDRs), one of the two objectives of the SNPP mission and one of the three elements in the JPSS mission objective. The other objective of the SNPP mission is risk reduction for the follow-on JPSS missions. This paper summarizes the on-orbit geolocation calibration and validation (Cal/Val) activities for both VIIRS sensors onboard SNPP and NOAA-20 in the past 10 years. These activities include nominal geolocation Cal/Val activities, risk reduction activities, and improvements for the on-orbit VIIRS sensor operations. After these activities, sub-pixel geolocation accuracy is achieved. Nadir equivalent geolocation uncertainty is generally within 75 m (1-σ), or 20% imagery band pixels, in either the along-scan or along-track direction for both SNPP and NOAA-20 VIIRS sensors. The worst 16-day measured geolocation errors (radial, 3-σ) are 280 m and 267 m, respectively, in the latest SNPP and NOAA-20 VIIRS data collections, which are better than the required accuracy of 375 m (radial, 3-σ). The risk reduction activities also improved VIIRS builds for JPSS-3 and JPSS-4 satellites, and provide lessons learned for other VIIRS-like sensor builds.

VIIRS↗
NASA NTRSDate not supplied

NASA’s Rotating Detonation Rocket Engine Development

The Rotating Detonation Rocket Engine has maintained steady development at NASA with many staggering performance advantages demonstrated to date over the state-of-the-art (SOA). The implementation of additive manufacturing and specialized NASA developed alloys have enabled rapid maturation of the technology. Several hot fire test projects have been successfully conducted at Marshall Space Flight Center under an early career initiative project funded by NASA Space Technology Mission Directorate. In addition, a new start Technology Demonstration Mission (TDM) project has been funded to investigate challenges relating to integration of turbomachinery with an RDRE thrust chamber assembly. This engine system demonstration will leverage a methane/oxygen single shaft turbopump with fuel rich gas generator and a 10,000 lbf thrust chamber assembly. The configuration was down selected based on feedback from both US industry collaborators and power balance trades in combination with technical feasibility. To date, industry has identified several use cases for RDRE ranging from thruster to primary launch vehicle propulsion. A wide range of fuel and oxidizers were also identified including but not limited to Methane, Kerosene and other liquid hydrocarbon (LH) fuels, and hydrogen. Recent work at NASA and in partnership with NASA has investigated these major fuels of interest with oxygen, air, and hydrogen peroxide (HTP) for various applications. NASA Marshall has already investigated the use of hydrogen/oxygen, methane/oxygen, kerosene/oxygen, and has plans in partnership with industry and academia to investigate LH/air and LH/HTP. In addition to propellants, hardware geometry has been investigated with some critical lessons learned toward greater theoretical performance over the SOA. To this end, several experimental and computational activities are ongoing to further advance the RDRE towards flight missions. Given the rate of advancement, it is highly likely the technology will be flown in space mission in the coming decade. This work documents and overviews many of these investigations and overviews NASA’s future plans for the technology maturation.

Thomas Teasley↗
DOE OSTI2025

Two-dimensional ring polymer molecular dynamics determination of the MnO + + H 2 /D 2 reaction rates on a Δ-machine learned potential energy surface

In this work, we investigate the impact of nuclear quantum effects in the kinetics of the MnO + + H 2 reaction, a prototypical system for gas-phase H 2 activation by transition metal oxide ions. The DFT based potential energy surfaces (PESs) for the lowest-lying quintet and septet spin states reported in our previous work are improved by 2953 newly calculated CCSD(T)/AVDZ points using a delta-machine learning (Δ-ML) method. To examine nuclear quantum effects, the rate coefficients are computed using ring-polymer molecular dynamics (RPMD). Due to the mechanistic complexity of the reaction, two reaction coordinates are necessary to map out the free-energy surface and an extended RPMD rate theory is developed. The calculated RPMD rate coefficients on the new PES are in better agreement with experimental data. The calculated kinetic isotope effects (KIEs) range from 1.6 to 1.8, also in good agreement with the experimental values. Additionally, the converged RPMD rate coefficients are 2.3 to 3.1 times higher than their classical counterparts across the studied temperature range, suggesting the presence of moderate nuclear quantum effects in this reaction.

Liu, Yang [University of New Mexico, Albuquerque, ↗
DOE OSTI2026

Unraveling Fundamental Activity–Stability Relationships in Rutile Oxides

The oxygen evolution reaction (OER) is a key anodic half-cell reaction that accompanies several critical electrochemical reduction reactions of interest to a variety of applications. Despite steady advances in understanding and qualitatively predicting OER activity and selectivity trends, a comprehensive description or prediction of material aqueous (in)stability and degradation mechanisms remains elusive, even though these processes critically influence device lifetime and economic feasibility. In this work, we investigate the interplay, or lack thereof, between OER activity and material aqueous stability across rutile oxides, with a particular focus on iridium oxide (IrO 2 ). By applying a Born–Haber cycle, we calculate the thermodynamic driving force for metal dissolution as a function of the applied bias and electrolyte conditions. We apply interpretable machine learning techniques, including principal component analysis and symbolic regression, to analyze trends across rutile oxides and find that key thermodynamic descriptors for OER activity and surface stability are only very weakly correlated. Instead, the local atomic environment─especially electronic structure signatures for interactions between the active site and its neighbors─plays a more important role in predicting material stability. Leveraging these insights, we investigate the impact of doping IrO 2 with a range of transition metals and show that the stability of Ir active sites can be tuned largely independently of its predicted OER activity. These insights lay the foundation for material design to improve stability with respect to corrosion, with the ultimate aim to enhance long-term stability without sacrificing catalytic performance in the OER.

evolution reactions↗
NASA NTRSDate not supplied

Distributed Lunar Data Platform with Advanced Machine Learning Capabilities in Support of Lunar Science and Exploration

The United States 2020 Space Policy directive declares that NASA, in cooperation with private industry, will “extend human economic activity into deep space by establishing a permanent human presence on the Moon”. This goal will require advanced data management, as well as analysis, modeling and representation of lunar information in order to prepare for Artemis human missions, lunar science investigations and exploration. To meet this requirement, we conceptualize and present an implementation strategy for a distributed platform for lunar data retrieval, inferencing and analysis, which will be based on federated learning and the NASA Celestial Mapping System (CMS). In addition to demonstrating the imperative of enabling lunar-borne data to remain in-situ but still accessible, this presentation will also include examples of how third parties could contribute both datasets and new functionality into this platform using an AI-based data import pipeline and a plug-in architecture respectively.

Artificial Intelligence↗
NASA NTRS1986

An application of adaptive learning to malfunction recovery

A self-organizing controller is developed for a simplified two-dimensional aircraft model. The Controller learns how to pilot the aircraft through a navigational mission without exceeding pre-established position and velocity limits. The controller pilots the aircraft by activating one of eight directional actuators at all times. By continually monitoring the aircraft's position and velocity with respect to the mission, the controller progressively modifies its decision rules to improve the aircraft's performance. When the controller has learned how to pilot the aircraft, two actuators fail permanently. Despite this malfunction, the controller regains proficiency at its original task. The experimental results reported show the controller's capabilities for self-organizing control, learning, and malfunction recovery.

Cruz, R. E.↗
NASA NTRS1993

Application of fuzzy logic-neural network based reinforcement learning to proximity and docking operations: Special approach/docking testcase results

As part of the RICIS project, the reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Maximum Mission (SMM) satellite simulation. In utilizing these fuzzy learning techniques, we use the Approximate Reasoning based Intelligent Control (ARIC) architecture, and so we use these two terms interchangeably to imply the same. This activity is carried out in the Software Technology Laboratory utilizing the Orbital Operations Simulator (OOS) and programming/testing support from other contractor personnel. This report is the final deliverable D4 in our milestones and project activity. It provides the test results for the special testcase of approach/docking scenario for the shuttle and SMM satellite. Based on our experience and analysis with the attitude and translational controllers, we have modified the basic configuration of the reinforcement learning algorithm in ARIC. The shuttle translational controller and its implementation in ARIC is described in our deliverable D3. In order to simulate the final approach and docking operations, we have set-up this special testcase as described in section 2. The ARIC performance results for these operations are discussed in section 3 and conclusions are provided in section 4 along with the summary for the project.

Jani, Yashvant↗
NASA NTRS2004

College Bound with the Office of Educational Programs

The Educational Programs Office at NASA Glenn Research Center hosts a variety of programs that takes on the hard task of getting students of all ages interested in pursuing careers in science, mathematics, and engineering. To help assist students along the way there are many programs to participate in such as: the explorers, shadowing opportunities, and paid internships. The Educational Programs Office not only creates learning opportunities for students, they also host workshops to help educators enhance their knowledge these fields. This summer I assisted Marie Borowski in the Educational Programs Office with the Tennessee State University College Bound Program. The Tennessee state University College Bound Program is an intensive two-week summer academic workshop designed to introduce minority students to the profession of engineering. NASA Glenn Research Center sent forty dedicated students on a bus to Nashville, Tennessee to experience college life as a whole. At the college the students day consisted of a math class, aeronautics, ACT/SAT preparation, writing and research, African American Culture, computer science, and study sessions. The students also went on educational field trips to the Fisk Museum, the Space and Rocket Center, and the Parthenon Museum. On the last day of the program the students competed in an oratorical contest where the students made a Powerpoint presentation on the class that they enjoyed the most. There were many processes that had to be put into action for the college bound program to run smoothly. The process started in early January with the preparation of applications. Once prepared, the applications were then sent to schools and past participants in hopes of receiving a well-qualified pool of applicants. Once the applications were received, a prescreening is done which ensures all of the information is complete. Then, they are reviewed by a panel, using a rubric to evaluate them, and the semifinalists are then selected. Interviews are held with the students and their parents had to be interviewed by a panel of judges and graded on a rubric. The scores were added up and the forty students were selected. My job this summer was getting the students ready to leave for Tennessee. My job consisted of working very closely with my mentor, Marie Borowski, compile the student data to provide it to the chaperones, TSU records, and NASA records. I learned about the vital communication between the NASA and the TSU program managers. After all the planning was done and the program had begun I had a chance to fly to Tennessee for six days to observe the students daily activities. The students had adjusted very well to the intense schedule, and seemed very enthusiastic about the activities to follow. The whole group was very attentive and enthusiastic program be longer. My goals for the summer were all met. I wanted to learn and retain all the information I possibly could on the job I was given. I was very happy with the end result.

Neal, Brittany D.↗
DOE OSTI2024

Impedance-Aware Graph Convolutional Networks for Voltage Estimation in Active Distribution Networks

Voltage estimation plays a key role in ensuring the effective control and reliability of distribution networks. However, traditional machine learning methods often fail to capture the details of the distribution network’s topology. To overcome this challenge, graph convolutional networks (GCN) have emerged as an alternative. Graph convolutional networks inherently capture the topology of the grid, utilizing correlations to achieve precise voltage estimation. Other machine learning models and conventional GCNs fail to account for the distribution line characteristics found in the real world, limiting their effectiveness. This paper proposes an advanced variant of GCN called the Impedance-Aware Graph Convolutional Network (IA-GCN). The IA-GCN layer incorporates the magnitude of the impedance into the graph convolution mechanism, allowing it to capture topological nuances and provide valuable insights into node interrelationships by considering impedance as an intrinsic dimension. The performance of the IA-GCN layer is then compared with that of GCN and GraphSAGE layers through a surrogate model for voltage estimation. The performance analysis demonstrates that IA-GCN outperforms GCN by reducing the MAE by 87.55% and improving the R-squared value by 98%.

Ravi, Abhijith↗
NASA NTRSDate not supplied

Can Large Strategic Science Missions Benefit from Class-D/SmallSat Lesson’s Learned?

This presentation is a set of charts presented at the 2024 AIAA ASCEND conference. This report is the result of an activity sponsored by Dr. Wanda Peters, Deputy Associate Administrator of Programs, who tasked Florence Tan and Carolyn Mercer with gathering insights from SmallSat/ Class D missions that could be extrapolated to enhance Class A-C missions.

SmallSats↗
NASA NTRS2021

Large-Scale High-Resolution Coastal Mangrove Forests Mapping Across West Africa With Machine Learning Ensemble and Satellite Big Data

Coastal mangrove forests provide important ecosystem goods and services, including carbon sequestration, biodiversity conservation, and hazard mitigation. However, they are being destroyed at an alarming rate by human activities. To characterize mangrove forest changes, evaluate their impacts, and support relevant protection and restoration decision making, accurate and up-to-date mangrove extent mapping at large spatial scales is essential. Available large-scale mangrove extent data products use a single machine learning method commonly with 30 m Landsat imagery, and significant inconsistencies remain among these data products. With huge amounts of satellite data involved and the heterogeneity of land surface characteristics across large geographic areas, finding the most suitable method for large-scale high-resolution mangrove mapping is a challenge. The objective of this study is to evaluate the performance of a machine learning ensemble for mangrove forest mapping at 20 m spatial resolution across West Africa using Sentinel-2 (optical) and Sentinel-1 (radar) imagery. The machine learning ensemble integrates three commonly used machine learning methods in land cover and land use mapping, including Random Forest (RF), Gradient Boosting Machine (GBM), and Neural Network (NN). The cloud-based big geospatial data processing platform Google Earth Engine (GEE) was used for pre-processing Sentinel-2 and Sentinel-1 data. Extensive validation has demonstrated that the machine learning ensemble can generate mangrove extent maps at high accuracies for all study regions in West Africa (92%–99% Producer’s Accuracy, 98%–100% User’s Accuracy, 95%–99% Overall Accuracy). This is the first-time that mangrove extent has been mapped at a 20 m spatial resolution across West Africa. The machine learning ensemble has the potential to be applied to other regions of the world and is therefore capable of producing high-resolution mangrove extent maps at global scales periodically.

coastal environment↗
NASA NTRS2000

Air, Ocean and Climate Monitoring Enhancing Undergraduate Training in the Physical, Environmental and Computer Sciences

Faculty in the Department of Physical, Environmental and Computer Sciences strongly believe in the concept that undergraduate research and research-related activities must be integrated into the fabric of our undergraduate Science and Technology curricula. High level skills, such as problem solving, reasoning, collaboration and the ability to engage in research, are learned for advanced study in graduate school or for competing for well paying positions in the scientific community. One goal of our academic programs is to have a pipeline of research activities from high school to four year college, to graduate school, based on the GISS Institute on Climate and Planets model.

Hope, W. W.↗
NASA NTRS2011

Mission X: Train Like an Astronaut. International Fitness Challenge

The Mission X, Train like an Astronaut, pilot project was a 2-year effort directed by the International Life Science Working Group. The pilot was funded by the Human Research Program and was lead by the Human Research Program Education and Outreach (HRPEO) project and supported by a group of space agencies providing in-kind resources. The aim was to identify an international educational outreach concept that would promote a life science topic utilizing the education and outreach expertise of the various space agencies working on the utilization of the International Space Station. This in turn serves as an inspiration for the younger generation to aspire to go further in school, and provides insight into the capability of a participating country to ensure the effort provided value for their communities and children. The pilot project developed the necessary tools to promote communications between the partners and to use materials and expertise from all the countries? space agencies. The Mission X Website (trainlikeanastronaut.org) provided a single repository for the educational activities as well as a place for the Challenge Teams to provide their progress in the international fitness challenge. It also added to the International flavor as different countries were able to share and learn about what was happening with all those involved in the 6-week challenge period. A point system was utilized to promote constructive, cooperative competition in which 4164 students participated. The points were used to help FitKid, Astro Charlie, "Walk-To-The-Moon". The 18 physical and educational Mission X activities were made available on the Mission X website in seven languages. The Mission X pilot project was considered a success in 1) the design, development, and implementation of the multi-language website, 2) the expansion of healthy lifestyle awareness, and 3) the concept for drawing an international educational community together to highlight global topics in association with human space exploration. Metrics (70 post-event surveys) and lessons learned were collated and assessed. Key Lessons Learned were 1) foster and strengthen partnerships at all levels, 2) enhance and standardize MX Training, and 3) further expansion of activities and methods to teach the importance of fitness and nutrition. Methods used to teach the importance of nutrition and fitness are provided in the report.

Lloyd, Charles↗
DOE OSTI2026

Solid-state NMR and theoretical studies illuminate lanthanum borohydride C–H borylation catalysts confined within a zeolite

Zeolite-supported single-site lanthanum borohydride catalyzes C–H borylation of hydrocarbons, while the related silica-supported complex is inactive under comparable conditions. The identical composition of support-La(BH 4 ) 2 (THF) 2 sites in the two materials implies that the support bestows underlying structural features onto La that are required for bond activation catalysis, yet the nature of these effects, which could include confinement effects and/or electronic modulation of the site itself, remains to be identified. We used solid-state nuclear magnetic resonance (SSNMR) spectroscopy and molecular dynamics simulations with machine-learning potentials (ML-MD) to analyze the electronic and steric effects imparted by the faujasite support on the precatalyst structure to correlate with catalytic activity. ML-MD simulations show that THF dissociates from La under the influence of confinement, leading to coordinatively unsaturated sites in the zeolite pores. Then, the La complex grafts on Brønsted acid sites (La BAS ) or isolated silanols (La SiO ) or remains physisorbed in the zeolite pores. Catalytic studies comparing compounds supported on faujasite zeolites containing or lacking BAS and/or silanols show that only the former complexes lead to active sites, ruling out confinement as the sole requirement for catalysis. The DFT calculations and ML-MD simulations also reveal that the surface-lanthanum coordination number is two (bidentate) for La BAS , with the metal center forming long, flexible bonds to two oxygen atoms bridging Si and Al, but only one oxygen atom (monodentate) for La SiO . The structure–activity relationship identifies confined, BAS-grafted species as active sites and provides important guidance for the design of enhanced atom-efficient catalysts.

Cui, Jinlei [Ames Laboratory, and Iowa State Univ.↗
DOE OSTI2024

Vehicle Position Detection Based on Machine Learning Algorithms in Dynamic Wireless Charging

Dynamic wireless charging (DWC) has emerged as a viable approach to mitigate range anxiety by ensuring continuous and uninterrupted charging for electric vehicles in motion. DWC systems rely on the length of the transmitter, which can be categorized into long-track transmitters and segmented coil arrays. The segmented coil array, favored for its heightened efficiency and reduced electromagnetic interference, stands out as the preferred option. However, in such DWC systems, the need arises to detect the vehicle’s position, specifically to activate the transmitter coils aligned with the receiver pad and de-energize uncoupled transmitter coils. This paper introduces various machine learning algorithms for precise vehicle position determination, accommodating diverse ground clearances of electric vehicles and various speeds. Through testing eight different machine learning algorithms and comparing the results, the random forest algorithm emerged as superior, displaying the lowest error in predicting the actual position.

47 OTHER INSTRUMENTATION↗