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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Staying Motivated During Tough Times

This paper describes the problem of team motivation on a project. Our team was working with the Department of Homeland Security (DHS). The task consisted of figuring out how to safely control and land an airliner using just the thrust from the engines. This is called Throttles-Only Control (TOC). We weren't allowed to modify the airliner in any way, given the time and cost involved, and we had to use a stock airliner with line pilots. The idea was to give the pilots an emergency checklist which would provide them with the most useful information in the shortest time to learn how to fly TOC. The DHS Program office that was supporting us had its funding redirected, due to new priorities. The process of staying motivated for finishing as much of the project as possible is described.

Cole, Jennifer H.↗
DOE OSTI2020

Implementing Mixed Reality Tools to Support Mission Delivery at Hanford - 20520

Mission Support Alliance's (MSA) Public Works and Information Systems organizations have been working with Microsoft Corporation to develop and implement mixed reality operational solutions using their HoloLens technology. This collaborative effort is intended to drive innovative solutions and significantly improve MSA's efficiency in performing work at the Hanford Site. HoloLens, a mixed reality tool, is a commercial off-the-shelf technology that combines a head-mounted viewing screen with applications to help people and organizations learn, communicate, and collaborate more effectively through the use of mixed reality. https://microsoft.com/en-us/hololens MSA's initial pilot successfully demonstrated its ability to use mixed reality utilizing HoloLens' advanced features in a variety of ways, illustrating the potential efficiency gains previously stated as goals of this pilot. These achievements include the development of proprietary technology that allows synchronization between a HoloLens device and a mobile device or alternate global positioning system (GPS) device. This synchronization enables the HoloLens device to access real time GPS location data anywhere on the Hanford Site. This real time location data, combined with MSA's improved Hanford Geographical Information System (GIS) data, enables the mixed reality pilot application to identify underground utility systems as well as related utility attributes. This ability will aid utility workers in several areas, including excavation activities, the placement of large cranes, identifying interactions and conflicts with future underground utility placements and, eventually, recognizing and de-conflicting tank waste transfer system valve alignments. The HoloLens mixed reality application is also able to provide full or small scale holographic images of a facility's digital twin. This feature will allow MSA's Water and Sewer Utilities team to train and validate procedures associated with the new water treatment facility virtually, before it is constructed. This reduces the amount of time it takes to complete the training and procedure validation, and accelerates the overall construction schedule. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Accelerating Ab Initio Simulation via Nested Monte Carlo and Machine Learned Reference Potentials

As a corollary of the rapid advances in computing, ab initio simulation is playing an increasingly important role in modeling materials at the atomic scale. Two strategies are possible, ab initio Monte Carlo (AIMC) and molecular dynamics (AIMD) simulation. The former benefits from exact sampling from the correct thermodynamic distribution, while the latter is typically more efficient with its collective all-atom coordinate updates. In this study, using a relatively simple test model comprised of helium and argon, we show that AIMC can be brought up to, and even above, the performance levels of AIMD via a hybrid nested sampling/machine learning (ML) strategy. Here, ML provides an accurate classical reference potential (up to three-body explicit interactions) that can pilot long collective Monte Carlo moves that are accepted or rejected in toto à la nested Monte Carlo (NMC); this is in contrast to the single move nature of a naive implementation. Our proposed method only requires a small up front expense from evaluating the ab initio energies and forces of (100) random configurations for training. Importantly, our method does not totally rely on the trained, assuredly imperfect, interaction. We show that high performance and exact sampling at the desired level of theory can be realized even when the trained interaction has appreciable differences from the ab initio potential. Remarkably, at the highest levels of performance realized via our approach, a pair of statistically uncorrelated atomic configurations can be generated with (1) ab initio calculations.

74 ATOMIC AND MOLECULAR PHYSICS↗
NASA NTRS1963

Integrated Mission Simulation for Long Term Space Flight

The problems of evaluating pilot performance, pilot training, and the integration of the man-machine system for proper functioning become increasingly difficult as the length of projected space flights increases. Further, actual in-flight experience becomes harder to obtain and the major means for training and evaluation becomes simulation. The first major source of difficulty in any simulation stems from a basic requirement of leaning theory: that for increased proficiency of performance to occur within any biologically capable system, the stimuli which are the bases for the expected behavior must be presented in an amenable form to the learner. Further, the sequence of presentation of the pertinent stimuli must be such that the learner can form the appropriate concepts and interactions required for the ultimate use of the learned material. It can then be seen that two basic factors of learning complex skills are: 1) Fidelity of simulation of pertinent stimuli. 2) Appropriate sequencing of stimuli during learning for proper concept formation.

SPACE FLIGHT↗
NASA NTRS2011

Mission X: Train Like an Astronaut Pilot Study

Mission X: Train Like an Astronaut is an international educational challenge focusing on fitness and nutrition as we encourage students to "train like an astronaut." Teams of students (aged 8-12) learn principles of healthy eating and exercise, compete for points by finishing training modules, and get excited about their future as "fit explorers." The 18 core exercises (targeting strength, endurance, coordination, balance, spatial awareness, and more) involve the same types of skills that astronauts learn in their training and use in spaceflight. This first-of-its-kind cooperative outreach program has allowed 14 space agencies and various partner institutions to work together to address quality health/fitness education, challenge students to be more physically active, increase awareness of the importance of lifelong health and fitness, teach students how fitness plays a vital role in human performance for exploration, and inspire and motivate students to pursue careers in STEM fields. The project was initiated in 2009 in response to a request by the International Space Life Sciences Working Group. USA, Netherlands, Italy, France, Germany, Austria, Colombia, Spain, and United Kingdom hosted teams for the pilot this past spring, and Japan held a modified version of the challenge. Several more agencies provided input into the preparations. Competing on 131 teams, more than 3700 students from 40 cities worldwide participated in the first round of Mission X. OUTCOMES AND BEST PRACTICES Members of the Mission X core team will highlight the outcomes of this international educational outreach pilot project, show video highlights of the challenge, provide the working group s initial assessment of the project and discuss the future potential of the effort. The team will also discuss ideas and best practices for international partnership in education outreach efforts from various agency perspectives and experiences

Lloyd, Charles W.↗
NASA NTRSDate not supplied

First Impressions of Data Driven Printmaking: Preliminary Outcomes of an Art and Science Collaboration

Katie Baldwin (University of Alabama Huntsville) and Helen Parache (NASA Marshall Space Flight Center) collaborated on a pilot project with two upper division printmaking classes at the UAH Art Department in Spring 2023. Presentations and guided discussions focused on what artists and scientists can learn from each other, drawing from the larger discourse, and an introduction to artists that incorporate scientific data in their work. Parache and Baldwin facilitated a series of activities where students engaged with data to develop content. Each session ended with creating a plan with each student for a data-driven project utilizing printmaking which was shared in a talk open to the public. This pilot collaboration brought up themes of accessibility, social relevance, emotional truth, and creative solutions. Next steps include repeating the workshop with tweaks in Fall 2023 and strengthening relationships between artists and scientists co-located in Huntsville, Alabama.

data-driven art↗
NASA NTRS2014

Aviation Safety Risk Modeling: Lessons Learned From Multiple Knowledge Elicitation Sessions

Aviation safety risk modeling has elements of both art and science. In a complex domain, such as the National Airspace System (NAS), it is essential that knowledge elicitation (KE) sessions with domain experts be performed to facilitate the making of plausible inferences about the possible impacts of future technologies and procedures. This study discusses lessons learned throughout the multiple KE sessions held with domain experts to construct probabilistic safety risk models for a Loss of Control Accident Framework (LOCAF), FLightdeck Automation Problems (FLAP), and Runway Incursion (RI) mishap scenarios. The intent of these safety risk models is to support a portfolio analysis of NASA's Aviation Safety Program (AvSP). These models use the flexible, probabilistic approach of Bayesian Belief Networks (BBNs) and influence diagrams to model the complex interactions of aviation system risk factors. Each KE session had a different set of experts with diverse expertise, such as pilot, air traffic controller, certification, and/or human factors knowledge that was elicited to construct a composite, systems-level risk model. There were numerous "lessons learned" from these KE sessions that deal with behavioral aggregation, conditional probability modeling, object-oriented construction, interpretation of the safety risk results, and model verification/validation that are presented in this paper.

Luxhoj, J. T.↗
DOE OSTI2024

Process Optimization of Carbon Electrode Materials Manufacturing by Experimental Study and Machine Learning Techniques

Electrospun carbon fibers from coal have been investigated as electrodes for batteries and supercapacitors. Despite the excellent properties of coal-derived carbon fibers (CCNF) for energy storage devices, there still lacks systematic understanding on how various process parameters affect final electrode performances, which poses challenges to scale from pilot to high volume manufacturing. The goals of this project are twofold. First, we focuse on process optimization for converting a new precursor from powder river basin (PRB) coal, referred to as coal-based polyurethane (CPU) to CCNF using electrospinning. Second, different machine learning techniques will be examined using experimental data from this work and open literature. Specifically, for CPU the following process parameters need to be characterized and optimized in order to produce CCNFs with desirable mechanical integrity and physiochemical properties: precursor composition and viscosity, operating voltage and distance, oxidation and carbonization temperature and duration. Consequently, physiochemical properties of the fibers were characterized to correlate these process parameters with desirable electrochemical performance. Given the complex nature of the fiber production process, ML models are assessed for their ability to capture the nonlinear relationship between process parameters and the electrochemical properties in applications including supercapacitors. As such, we applied various machine learning techniques, to determine which technique produces a model that best predicts device function.

Cincotta, Robert E.F.↗
DOE OSTI2026

Development of a High-Efficiency Hybrid Dry Cooler System for sCO 2 Power Cycles in CSP Applications

This project addressed a major gap in supercritical CO 2 (sCO 2 ) power cycle research by focusing on the pre-cooler, a component that had received little attention despite its significant impact on cycle efficiency and plant economics. The team developed a compact dry cooler using brazed/diffusion-bonded microchannel passages paired with formed air-side fins, advancing the technology from TRL-2 toward commercial readiness. Compared to conventional fin-tube coolers, the design cuts installation footprint by roughly half for 10+ MWth systems while achieving better heat transfer and lower approach temperatures, translating into a projected LCOE reduction from 6.04 ¢/kWh to between 5.85 and 5.94 ¢/kWh. While fabrication of an aluminum MW-scale prototype revealed brazing and sealing challenges at larger scales, lessons learned informed a subsequent 1 MWth unit that was successfully built and delivered for integration into Sandia's Gen3 Particle Pilot Plant, advancing the technology to TRL-7 with a path toward TRL-8 pending successful testing.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2025

Predicting cutoff L-shells of solar protons using the GPPSn particle dataset

Solar energetic protons (SEPs) arriving at the Earth trigger severe radiation storms in the near-Earth space, directly impacting space missions operating at various altitudes. Therefore, monitoring SEP events and predicting the penetration depths of solar protons are critical for aerospace sectors. Building on previous efforts, here we demonstrate the feasibility of using proton measurements from the Global Prompt Proton Sensor network (GPPSn), enabled by Los Alamos National Laboratory developed combined X-ray dosimeters aboard GPS satellites, to characterize and predict the penetration of solar protons into the geomagnetic field. The inclined medium-Earth-orbits (MEOs) of the global GPS constellation offer a unique advantage of allowing simultaneous measurements of penetrating solar protons inside both open- and closed-field line regions. Therefore, the L-profiles of ∼10s–100 MeV solar protons and their associated cutoff L-shells can be determined from the GPPSn dataset, using predefined threshold proton flux values rather than traditional flux ratios. After examining a list of SEP event intervals across solar cycles 23, 24 and 25—including the 2024 Mother’s Day superstorm, we showcase how the latest GPPSn proton dataset (release v1.10), reprocessed and calibrated, can not only be used to monitor solar proton distributions inside the dynamic geomagnetic field for individual events, but also to derive a new empirical model linking cutoff L-shells with several key space weather parameters. This newly developed SEPCL-MEO model demonstrates high predictive performance; for example, predictions for > 30 MeV solar protons yield a correlation coefficient of 0.85 and performance efficiency of 0.67 when validated against GPPSn observations. Results from this pilot study underscores the scientific and operational value of the GPPSn dataset, and this dataset—when paired with machine-learning techniques—can play a critical role in observing and predicting the effects of future incoming SEP events, including extreme ones.

58 GEOSCIENCES↗
DOE OSTI2024

Jamming Detection for Low-Resolution SC-FDE Systems: A Machine Learning Approach

Jammers interfere with communication between base stations (BSs) and legitimate users, leading to degradation of wireless system performance. Our study focuses on jamming detection for wideband single-carrier frequency domain equalization (SC-FDE) systems with low-resolution analog-to digital converters (ADCs). In such systems, jamming detection is challenging because traditional analytical approaches cannot be directly applied due to the delay dispersion in wideband channels and the non-linearity induced by low-resolution ADCs. We propose a machine learning (ML)-based jamming detection method that directly uses the quantized receive signals. Significantly, our ML-based detector can be integrated into existing standard frameworks, such as unique word (UW)-based SC-FDE systems, as it uses existing pilots without requiring additional pilots for jamming detection. Through numerical simulations, we show that two or more bits provide satisfactory performance compared to unquantized scenarios. Additionally, we demonstrate that using more and well-separated pilot symbols improves performance.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Washington State Cyber Incident Response Summit Event Report

On September 7 and 8, 2022, Pacific Northwest National Laboratory and Washington State Adjutant General Major General Bret Daugherty, with support from the Department of Homeland Security Cybersecurity and Infrastructure Security Agency, hosted the Washington State Cyber Incident Response Summit at Camp Murray in Washington State. The invite-only summit convened 40 key decision makers and stakeholders from across the state to discuss strategies and pilot a collaborative approach to improve cyber incident response readiness within Washington. In small working groups, participants shared incident response lessons learned, best practices, and opportunities for improvement within the water and transportation sectors. This report highlights the details of the workshop presentations and discussions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Deep learning for morphological identification of extended radio galaxies using weak labels

Abstract The present work discusses the use of a weakly-supervised deep learning algorithm that reduces the cost of labelling pixel-level masks for complex radio galaxies with multiple components. The algorithm is trained on weak class-level labels of radio galaxies to get class activation maps (CAMs). The CAMs are further refined using an inter-pixel relations network (IRNet) to get instance segmentation masks over radio galaxies and the positions of their infrared hosts. We use data from the Australian Square Kilometre Array Pathfinder (ASKAP) telescope, specifically the Evolutionary Map of the Universe (EMU) Pilot Survey, which covered a sky area of 270 square degrees with an RMS sensitivity of 25–35 $\mu$ Jy beam $^{-1}$ . We demonstrate that weakly-supervised deep learning algorithms can achieve high accuracy in predicting pixel-level information, including masks for the extended radio emission encapsulating all galaxy components and the positions of the infrared host galaxies. We evaluate the performance of our method using mean Average Precision (mAP) across multiple classes at a standard intersection over union (IoU) threshold of 0.5. We show that the model achieves a mAP $_{50}$ of 67.5% and 76.8% for radio masks and infrared host positions, respectively. The network architecture can be found at the following link: https://github.com/Nikhel1/Gal-CAM

Astronomy & Astrophysics↗
NASA NTRS1996

The X-31A quasi-tailless flight test results

A quasi-tailless flight investigation was launched using the X-31A enhanced fighter maneuverability airplane. In-flight simulations were used to assess the effect of partial to total vertical tail removal. The rudder control surface was used to cancel the stabilizing effects of the vertical tail, and yaw thrust vector commands were used to restabilize and control the airplane. The quasi-tailless mode was flown supersonically with gentle maneuvering and subsonically in precision approaches and ground attack profiles. Pilot ratings and a full set of flight test measurements were recorded. This report describes the results obtained and emphasizes the lessons learned from the X-31A flight test experiment. Sensor-related issues and their importance to a quasi-tailless simulation and to ultimately controlling a directionally unstable vehicle are assessed. The X-31A quasi-tailless flight test experiment showed that tailless and reduced tail fighter aircraft are definitely feasible. When the capability is designed into the airplane from the beginning, the benefits have the potential to outweigh the added complexity required.

Bosworth, John T.↗
NASA NTRS2006

Enhancing Icing Training for Pilots Through Web-Based Multimedia

The Aircraft Icing Project of the NASA Aviation Safety Program has developed a number of in-flight icing education and training aids designed to increase pilot awareness about the hazards associated with various icing conditions. The challenges and advantages of transitioning these icing training materials to a Web-based delivery are discussed. Innovative Web-based delivery devices increased course availability to pilots and dispatchers while increasing course flexibility and utility. These courses are customizable for both self-directed and instructor-led learning. Part of our goal was to create training materials with enough flexibility to enable Web-based delivery and downloadable portability while maintaining a rich visual multimedia-based learning experience. Studies suggest that using visually based multimedia techniques increases the effectiveness of icing training materials. This paper describes these concepts, gives examples, and discusses the transitional challenges.

Fletcher, William↗
NASA NTRS2018

Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding

As spacecraft send back increasing amounts of telemetry data, improved anomaly detection systems are needed to lessen the monitoring burden placed on operations engineers and reduce operational risk. Current spacecraft monitoring systems only target a subset of anomaly types and often require costly expert knowledge to develop and maintain due to challenges involving scale and complexity. We demonstrate the effectiveness of Long Short-Term Memory (LSTMs) networks, a type of Recurrent Neural Network (RNN), in overcoming these issues using expert-labeled telemetry anomaly data from the Soil Moisture Active Passive (SMAP) satellite and the Mars Science Laboratory (MSL) rover, Curiosity. We also propose a complementary unsupervised and nonparametric anomaly thresholding approach developed during a pilot implementation of an anomaly detection system for SMAP, and offer false positive mitigation strategies along with other key improvements and lessons learned during development.

Soderstrom, Tom↗
NASA NTRS2023

Technology Scouting Phase 1 Report

Identifying emerging technologies is critical to a mission-oriented agency such as NASA that is always endeavoring to achieve first-of-a-kind human and scientific exploration goals. While NASA attempts to embrace faster and novel development approaches, there have been several recent examples where the Agency has been outpaced in technology research and development, especially by emerging companies. This is in part due to the democratization of space, as seen in the flood of new space companies and capital, and because capabilities and performance levels that used to be almost exclusively demanded by space missions are now scattered across many industries. Also, NASA tends to focus on technologies and techniques developed internally, within the space sector, or in clearly related disciplines. In this context, the overall goal of the Technology Scouting project is to develop an approach to conduct effective technology scouting within NASA by first assessing the need for such a capability, evaluating current and recommended approaches, and then developing a strategy to address any weaknesses in the current approach. The study was designed to be conducted in three phases, each with its own goals and objectives: - Phase 1: Validate whether NASA’s technology scouting capability is consistent with best practices from industry, academia, and other government organizations, and identify whether there is the appetite for adopting a more rigorous technology scouting capability across the Agency. - Phase 2: Identify specific opportunities and challenges to adopting a more comprehensive technology scouting program through a pilot effort. - Phase 3: Define the implementation approach for the more comprehensive technology scouting program that is in response to the information learned during the earlier phases of the study.

Ellen Gertsen↗
NASA NTRSDate not supplied

Helicopter Pilot Evaluations of the Airborne Collision Avoidance System Xr in a High-Fidelity Motion Simulation

New aircraft and aerial Urban Air Mobility operations require updated technologies to maintain vehicle separation during flight. Specifically, higher-density airspace will need more than traditional air traffic control to keep vehicles well clear of each other and avoid Near Midair Collisions. In response, the Federal Aviation Administration is currently developing the Airborne Collision Avoidance System X (ACAS X) for next-generation air traffic deconfliction, which provides caution-level and warning-level alerts in response to multiple aircraft types. This study recruited six helicopter pilots to fly an electric vertical takeoff and landing vehicle model in simulated operations under Visual Flight Rules (VFR). Flights were accomplished using the high-fidelity Vertical Motion Simulator at the NASA Ames Research Center. Participants controlled the vehicle using two side inceptors and foot rudders. The rotorcraft variant of ACAS X (ACAS Xr) was provided for alerting and guidance during traffic conflicts. Pilots used this system while giving feedback to the researchers through questionnaires, debriefs, and other discussions. Variables of interest to the study were phases of flight (i.e., Cruise, Hover, and Approach) and ACAS Xr configurations: The Collision Avoidance System configuration behaves similarly to current commercial traffic systems used for tactical deconfliction in crewed vehicles, and the Detect and Avoid configuration was developed to provide extra, corrective-level guidance for unmanned aircraft systems. Results showed that pilots found the alerting and guidance from ACAS Xr useful, effective, and acceptable for VFR operations. Certain elements, like speed guidance and text banners, were found to be of no use to the pilots. Hover and Approach scenarios were considered the most difficult for ACAS Xr alerting. Reasons for this difficulty were partially due to learning interference (i.e., overcoming previously learned behavior) and partially due to the vehicle model (i.e., NASA’s Lift Plus Cruise design). Still, alerting-based confounds reveal the need for more development for ACAS Xr during these Hover and Approach flight phases. Study caveats and future projects are discussed.

air taxis↗