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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Practical Applications of Cables and Ropes in the ISS Countermeasures System

National Aeronautics and Space Administration (NASA) uses exercise countermeasures on the International Space Station (ISS) to maintain crew health and combat the negative effects of long-duration spaceflight on the human body. Most ISS exercise countermeasures system (CMS) equipment rely heavily on the use of textile and wire ropes to transmit resistive loads and provide stability in a microgravity environment. For a variety of reasons, including challenges in simulating microgravity environments for testing and limits on time available for life cycle testing, the textiles and wire ropes have contributed significantly to on-orbit planned and unplanned maintenance time. As a result, continued ground testing and on-orbit experience since the first expedition on the ISS in 2000 provide valuable data and lessons learned in materials selection, applications, and design techniques to increase service life of these ropes. This paper will present a review of the development and failure history of textile and wire ropes for four exercise countermeasure systems-the Treadmill with Vibration Isolation and Stabilization (TVIS) System, Cycle Ergometer with Vibration Isolation and Stabilization (CEVIS) System, Interim Resistive Exercise Device (IRED), and the Advanced Resistive Exercise Device (ARED)-to identify lessons learned in order to improve future systems. These lessons learned, paired with thorough testing on the ground, offer a forward path towards reduced maintenance time and up-mass for future space missions.

Svetlik, Randall G.↗
NASA NTRS2019

Assessing ISS Habitability Training: A Historical Perspective

Over the lifetime of the International Space Station (ISS) training evolved based on Astronaut feedback, system updates, increased mission duration, and shifting operational goals. This training is a mixture of skill-based instruction and mission specific lessons on many topics. Habitability training specifically focuses on preparing astronauts for the day-to-day needs of living in space such as hygiene, food, sleep, restraints/mobility aids, and housekeeping. In this paper, the authors evaluate the effectiveness of habitability training for Astronaut working on the International Space Station (ISS) over the course of its continuously manned operation since 2000. This involved identifying ISS habitability training changes in lessons objectives and skills between revisions. These identified changes were then compared with relevant Astronaut feedback from post mission debriefs to determine the effectiveness of the changes. Habitability training is essential for Astronauts and has changed and continues to evolve over the course of the ISS. Understanding ISS habitability and how to most effectively train it will generate lessons learned that benefit current ISS operations and future exploration outside of Low Earth Orbit (LEO) to the moon and eventually Mars.

Swarmer, Tiffany M.↗
NASA NTRS2020

Modeling and Simulation - Virtual

The purpose of this report is to describe the body of work I have produced as a NASA Office of Science Technology Engineering and Math (OSTEM) intern in the Fall 2020 semester. My mentor during this session was Antonio Pego and my main task was to research and develop solutions in augmented and virtual reality technologies for use in training simulations. A main focus of this project was the development of human models for use in the game engine, with the purpose of creating simulations that would help with realistic analysis of tasks that will be performed by personnel. A steep learning curve had to be tackled in order to learn and catch up with current MR technologies. Along the way I created documentation of the different techniques utilized to support future continuation of the project.

Peter Leroy Santana Rodriguez↗
NASA NTRS2021

Assessment of the Advanced Very High Resolution Radiometer (AVHRR) for Snowfall Retrieval in High Latitudes Using CloudSat and Machine Learning

Precipitation retrieval is a challenging topic, especially in high latitudes (HL), and current precipitation products face ample challenges over these regions. This study investigates the potential of the Advanced Very High Resolution Radiometer (AVHRR) for snowfall retrieval in HL using CloudSat radar information and machine learning (ML). With all the known limitations, AVHRR observations should be considered for HL snowfall retrieval because 1) AVHRR data have been continuously collected for about four decades on multiple platforms with global coverage, and similar observations will likely continue in the future; 2) current passive microwave satellite precipitation products have several issues over snow and ice surfaces; and 3) good coincident observations between AVHRR and CloudSat are available for training ML algorithms. Using ML, snowfall rate was retrieved from AVHRR’s brightness temperature and cloud probability, as well as auxiliary information provided by numerical reanalysis. The results indicate that the ML-based retrieval algorithm is capable of detection and estimation of snowfall with comparable or better statistical scores than those obtained from the Atmospheric Infrared Sounder (AIRS) and two passive microwave sensors contributing to the Global Precipitation Measurement (GPM) mission constellation. The outcomes also suggest that AVHRR-based snowfall retrievals are spatially and temporally reasonable and can be considered as a quantitatively useful input to the merged precipitation products that require frequent sampling or long-term records.

Mohammad Reza↗
DOE OSTI2024

Predicting Drug Effects from High-dimensional Asymmetric Drug Data Sets using Graph Neural Networks: A Comprehensive Analysis of Multi-target Drug Effect Prediction

Graph neural networks (GNNs) have emerged as one of the most effective Machine learning (ML) techniques for drug effect prediction from drug molecular graphs. Despite having immense potential, GNN models lack performance when using data sets that contain high dimensional asymmetrically co-occurrent drug effects as targets with complex correlations between them. Training individual learning models for each drug effect and incorporating every prediction result for a wide spectrum of drug effects is beyond practicality. Such an implication provides a testbed to address this challenge as multi-target prediction problems, aiming to predict all drug effects at a time. We develop standard and hybrid graph neural networks (GNNs)to perform two separate tasks that are multi-regression for continuous values and multi-label classification for categorical values contained in our data sets. Since this step makes the target data even more sparse and introduces asymmetric label co-occurrence, the learning of multi-label classification models becomes difficult and heavily impacts the GNN's performance. To address these challenges, we propose a new data oversampling technique to improve multi-label classification performances on all the given imbalanced molecular graph data sets. Using the technique, we improve the data imbalance ratio of the drug effects better than before while protecting the data set's integrity. Finally, we evaluate multi-label classification performance using the best-performant hybrid GNN model on all the oversampled data sets obtained from the proposed oversampling technique. These results outperform those of other ML models including GNN models when they are trained on the original data sets or oversampled data sets using MLSMOTE (a well-known oversampling technique) in all evaluation metrics precision, recall, and F1 score by a significant margin.

Bose, Avishek [ORNL]↗
DOE OSTI2026

Operating advanced scientific instruments with AI agents that learn on the job

Advanced scientific user facilities, such as next generation X-ray light sources and self-driving laboratories, are revolutionizing scientific discovery by automating routine tasks and enabling rapid experimentation and characterizations. However, these facilities must continuously evolve to support new experimental workflows, adapt to diverse user projects, and meet growing demands for more intricate instruments and experiments. This continuous development introduces significant operational complexity, necessitating a focus on usability, reproducibility, and intuitive human-instrument interaction. In this work, we explore the integration of agentic AI, powered by Large Language Models (LLMs), as a transformative tool to achieve this goal. We present our approach to developing a human-in-the-loop pipeline for operating advanced instruments including an X-ray nanoprobe beamline and an autonomous robotic station dedicated to the design and characterization of materials. Specifically, we evaluate the potential of various LLMs as trainable scientific assistants for orchestrating complex, multi-task workflows, which also include multimodal data, optimizing their performance through optional human input and iterative learning. We demonstrate the ability of AI agents to bridge the gap between advanced automation and user-friendly operation, paving the way for more adaptable and intelligent scientific facilities.

Large Language Models↗
DOE OSTI2025

Machine Learning-Based Technique for Automated Sensor Characterization

The development of novel instrumentation requires an iterative cycle with three stages: design, prototyping, and testing. Recent advancements in simulation and nanofabrication techniques have significantly accelerated the design and prototyping phases. Nonetheless, detector characterization continues to be a major bottleneck in device development. During the testing phase, a significant time investment is required to characterize the device in different operating conditions and find optimal operating parameters. The total effort spent on characterization and parameter optimization can occupy a year or more of an expert s time. In this work, we present a novel technique for automated sensor calibration that aims to accelerate the testing stage of the development cycle. This technique leverages closed-loop Bayesian optimization (BO), using real-time measurements to guide parameter selection and identify optimal operating states. We demonstrate the method with a novel low-noise CCD, showing that the machine learning-driven tool can efficiently characterize and optimize operation of the sensor in a couple of days without supervision of a device expert.

Zepeda, Cuevas [Chicago U., KICP]↗
NASA NTRS1998

Development of Ultrasonic and Fabry-Perot Interferometer for Non-Destruction Inspection of Aging Aircraft

Fabry-Perot Interferometer (FPI) sensor detection system was continued and refined modifications were made in the data acquisition and evaluation process during the last year. The ultrasonic and FPI detection system was improved from one to multiple sensor detectors. Physical models were developed to understand the physical phenomenon of this work. Multilayered flawed samples were fabricated for inspection by a prototype ultrasonic and FPI detection. Experimental data was verified with simulated results. Undergraduate students that were associated with this research gained valuable knowledge from this experience. This was a learning process helping students to understand the importance of research and its application to solve important technological problems. As a result of our students exposure to this research two and planning to continue this type of research work in graduate school. A prototype instrument package was laboratory tested an actual airframe structure for documentation purposes.

Smith, Alphonso C.↗
NASA NTRS1999

Project VISION (Very Intensive Scientific Intercurricular On-Site Education

Project VISION (Very Intensive Scientific Intercurricular On-Site Education) is a joint effort among NASA/John F. Kennedy Space Center, Florida International University, Universidad del Turabo, Miami-Dade County Public Schools and the Caguas/Gurabo Public Schools in Puerto Rico. The project's main mission is to institutionalize change among the elementary and middle school science and math teachers at participating schools so that their students receive continuously enriched instruction in the principles of science and math through the use of hands-on and minds-on experiments called learning modules. These leaming modules incorporate the national science and math education standards provided by the National Committee on Science Education Standards and Assessments and the National Council of Teachers of Mathematics, respectively. The use of learning modules that require hands-on and minds-on activities in a classroom setting garners great enthusiasm and motivation on the part of the target students for the understanding of the lesson's underlying math and science principles. With this enthusiasm and motivation, comes acceptance, attention, participation, discipline, acquiescence, and collaboration. Additionally, the use of hands-on activities may also require learning through a gamut of senses. Not only can the student use his/her eyes and ears during these activities, but most times, they can also use their senses of touch, smell, and taste, as well as intuition. Learning is, therefore, achieved using most or all the human senses. The combination of motivation/enthusiasm and the use of multiple senses creates an ideal environment conducive to leaming at a profound level.

Roig, Gustavo A.↗
NASA NTRS2007

Clinical Core Competency Training for NASA Flight Surgeons

Introduction: The cohort of NASA flight surgeons (FS) is a very accomplished group with varied clinical backgrounds; however, the NASA Flight Surgeon Office has identified that the extremely demanding schedule of this cohort prevents many of these physicians from practicing clinical medicine on a regular basis. In an effort to improve clinical competency, the NASA FS Office has dedicated one day a week for the FS to receive clinical training. Each week, an FS is assigned to one of five clinical settings, one being medical patient simulation. The Medical Operations Support Team (MOST) was tasked to develop curricula using medical patient simulation that would meet the clinical and operational needs of the NASA FS Office. Methods: The MOST met with the Lead FS and Training Lead FS to identify those core competencies most important to the FS cohort. The MOST presented core competency standards from the American Colleges of Emergency Medicine and Internal Medicine as a basis for developing the training. Results: The MOST identified those clinical areas that could be best demonstrated and taught using medical patient simulation, in particular, using high fidelity human patient simulators. Curricula are currently being developed and additional classes will be implemented to instruct the FS cohort. The curricula will incorporate several environments for instruction, including lab-based and simulated microgravity-based environments. Discussion: The response from the NASA FS cohort to the initial introductory class has been positive. As a result of this effort, the MOST has identified three types of training to meet the clinical needs of the FS Office; clinical core competency training, individual clinical refresher training, and just-in-time training (specific for post-ISS Expedition landings). The MOST is continuing to work with the FS Office to augment the clinical training for the FS cohort, including the integration of Web-based learning.

Polk, J. D.↗
NASA NTRS2004

Execution of the Spitzer In-orbit Checkout and Science Verification Plan

The Spitzer Space Telescope is an 85-cm telescope with three cryogenically cooled instruments. Following launch, the observatory was initialized and commissioned for science operations during the in-orbit checkout (IOC) and science verification (SV) phases, carried out over a total of 98.3 days. The execution of the IOC/SV mission plan progressively established Spitzer capabilities taking into consideration thermal, cryogenic, optical, pointing, communications, and operational designs and constraints. The plan was carried out with high efficiency, making effective use of cryogen-limited flight time. One key component to the success of the plan was the pre-launch allocation of schedule reserve in the timeline of IOC/SV activities, and how it was used in flight both to cover activity redesign and growth due to continually improving spacecraft and instrument knowledge, and to recover from anomalies. This paper describes the adaptive system design and evolution, implementation, and lessons learned from IOC/SV operations. It is hoped that this information will provide guidance to future missions with similar engineering challenges

operations↗
NASA NTRS2003

Grease-Resistant O Rings for Joints in Solid Rocket Motors

There is a continuing effort to develop improved O rings for sealing joints in solid-fuel rocket motors. Following an approach based on the lessons learned in the explosion of the space shuttle Challenger, investigators have been seeking O-ring materials that exhibit adequate resilience for effective sealing over a broad temperature range: What are desired are O rings that expand far and fast enough to maintain seals, even when metal sealing surfaces at a joint move slightly away from each other shortly after ignition and the motor was exposed to cold weather before ignition. Other qualities desired of the improved O rings include adequate resistance to ablation by hot rocket gases and resistance to swelling when exposed to hydrocarbon-based greases used to protect some motor components against corrosion. Five rubber formulations two based on a fluorosilicone polymer and three based on copolymers of epichlorohydrin with ethylene oxide were tested as candidate O-ring materials. Of these, one of the epichlorohydrin/ethylene oxide formulations was found to offer the closest to the desired combination of properties and was selected for further evaluation.

Harvey, Albert R.↗
NASA NTRS2014

Model-Based GN and C Simulation and Flight Software Development for Orion Missions beyond LEO

For Orion missions beyond low Earth orbit (LEO), the Guidance, Navigation, and Control (GN&C) system is being developed using a model-based approach for simulation and flight software. Lessons learned from the development of GN&C algorithms and flight software for the Orion Exploration Flight Test One (EFT-1) vehicle have been applied to the development of further capabilities for Orion GN&C beyond EFT-1. Continuing the use of a Model-Based Development (MBD) approach with the Matlab®/Simulink® tool suite, the process for GN&C development and analysis has been largely improved. Furthermore, a model-based simulation environment in Simulink, rather than an external C-based simulation, greatly eases the process for development of flight algorithms. The benefits seen by employing lessons learned from EFT-1 are described, as well as the approach for implementing additional MBD techniques. Also detailed are the key enablers for improvements to the MBD process, including enhanced configuration management techniques for model-based software systems, automated code and artifact generation, and automated testing and integration.

Odegard, Ryan↗
NASA NTRS1991

Structural integrity of future aging airplanes

A multitude of design considerations is involved in ensuring the structural integrity of Boeing jet transports that have common design concepts validated by extensive analyses, tests, and three decades of service. As airplanes approach their design service objectives, the incidences of fatigue and corrosion may become widespread. Continuing airworthiness of the aging jet fleet requires diligent performance from the manufacturer, the airlines, and airworthiness authorities. Aging fleet support includes timely development of supplemental structural inspection documents applicable to selected older airplanes, teardown inspections of high-time airframes retired from service, fatigue testing of older airframes, and structural surveys of more than 130 airplanes operated throughout the world. Lessons learned from these activities are incorporated in service bulletin recommendations, production line modifications, and design manual updates. An overview of traditional Boeing fleet support activities and the anticipated benefits for future generations of commercial airplanes based on the continuous design improvement process are presented.

Aging Aircraft↗
NASA NTRSDate not supplied

Mission Status, On-Orbit Performance, and Lessons Learned of the Global Ecosystem Dynamics Investigation (GEDI) Lidar Laser Transmitters

The Global Ecosystems Dynamics Investigation (GEDI) Lidar, is a global 3-dimensional biomass lidar instrument aboard the International Space Station (ISS) and the Japanese Experiment Module (JEM). Its core mission is to measure the global carbon balance of Earth’s forests with 3 laser transmitters using multi-beam waveform-capture methods. GEDI’s laser transmitter concept was originally funded by NASA Goddard’s Earth Science Technology Office (ESTO) for future Earth Altimetry and Lidar missions ~ 20 years ago, and after a long road of development, the first flight-ready unit was a 10 mJ-class system for GEDI’s ISS-based specifications. Furthermore, no adjustment in drive parameters has been required nor significant decay detected after 4 years of near continuous operation of 3 on-board lasers, which were designed for a 2-year mission. We report on the GEDI laser design process, their on-orbit performance, the major lessons learned, and some critical insight into our in-house flight quality development process that enabled their delivery within budget, schedule, and low risk extended mission life capability.

D. Barry Coyle↗
DOE OSTI2026

Evaluation of Hardware and Software Bill of Materials (HBOMs/SBOMs) Extraction Methods

Hardware and software bills of materials (HBOMs and SBOMs) provide important visibility into the components, dependencies, and supply chain relationships within programmable digital devices. This visibility is critical for advanced nuclear reactor applications, where use of common or shared hardware components, software libraries, suppliers, or manufacturing processes may create common cause failure (CCF) vulnerabilities despite apparent diversity. This paper evaluates current approaches for obtaining and analyzing HBOMs and SBOMs in support of CCF, diversity and defense-in-depth (D3) assessments, and begins to explore potential methods for artificial intelligence/machine learning-based analysis. The availability of BOM information from advanced reactor manufacturers and vendors, representative hardware and software categories found in advanced reactor systems continues to limit research [13]. This paper compares commonly used BOM formats, including CycloneDX, SPDX, and SWID. It also surveys publicly available tools for generating BOMs from source code, compiled binaries, and hardware-related information, noting limitations in language coverage, system age, and format interoperability. Finally, this paper evaluates methods for correlating BOM data with vulnerability and exploitability information, including VEX, CVE, and CWE resources. The findings indicate that publicly available nuclear-vendor BOMs are limited, making third-party extraction and research into novel analysis techniques necessary.

Cybersecurity↗
NASA NTRS1992

Development and flight history of SERT 2 spacecraft

A 25-year historical review of the Space Electric Rocket Test 2 (SERT 2) mission is presented. The Agena launch vehicle; the SERT 2 spacecraft; and mission-peculiar spacecraft hardware, including two ion thruster systems, are described. The 3 1/2-year development period, from 1966 to 1970, that was needed to design, fabricate, and qualify the ion thruster system and the supporting spacecraft components, is documented. Major testing of two ion thruster systems and related auxiliary experiments that were conducted in space after the 3 Feb. 1970, launch are reviewed. Extended ion thruster restarts from 1973 to 1981 are reported, in addition to cross-neutralization tests. Tests of a reflector erosion experiment were continued in 1989 to 1991. The continuing performance of spacecraft subsystems, including the solar arrays, over the 1970-1991 period is summarized. Finally, the knowledge of thruster-spacecraft interactions learned from SERT 2 is listed.

Kerslake, William R.↗
NASA NTRS1992

Development and flight history of SERT 2 spacecraft

A 25-year historical review of the Space Electric Rocket Test 2 (SERT 2) mission is presented. The Agena launch vehicle; the SERT 2 spacecraft; and mission-peculiar spacecraft hardware, including two ion thruster systems are described. The 3 1/2-year development period, from 1966 to 1970, that was needed to design, fabricate, and qualify the ion thruster system and the supporting spacecraft components, is documented. Major testing of two ion thruster systems and related auxiliary experiments that were conducted in space after the 3 Feb. 1970, launch are reviewed. Extended ion thruster restarts from 1973 to 1981 are reported, in addition to cross-neutralization tests. Tests of a reflector erosion experiment were continued in 1989 to 1991. The continuing performance of spacecraft subsystems, including the solar arrays, over the 1970-1991 period is summarized. Finally, the knowledge of thruster-spacecraft interactions learned from SERT 2 is listed.

Kerslake, William R.↗