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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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NASA NTRS1994

Design and implementation of two two-week Teacher Enhancement Institutes

During this summer, I have been part of a four team effort that planned and executed two two-week Teacher Enhancement Institutes (TEI) for 40 K-8 teachers from this area. The TEI was designed to enhance teachers' background in aeronautics and technology so that they would be better equipped to encourage and to train students in the mathematics, science, and technology fields. The teachers were given a stipend and three graduate credits from Christopher Newport University for their participation in this program. The four ASEE fellows worked together to develop objectives and a schedule of activities for each two-week session based on the program outline given in the grants that were funding this effort. We divided the responsibilities in coordinating and implementing each part of the TEI based on the specific strengths and background of each ASEE fellow. My specific responsibilities were: (1) to develop the course syllabus and generally handle all matters involved with the graduate course; (2) coordinate the follow-up sessions; and (3) design and manage half of the technology sessions that we had scheduled (approximately 30% of the TEI was devoted to technology). Because the first two responsibilities were primarily administrative in nature, I will address only the last. The technology sessions were divided into computer-only and other technologies (e.g., television and digital technology including scanning, digital photography and CD-ROM). I had responsibility for the computer-only technology sessions. The emphasis of these sessions was on use of the Internet specifically to locate and use educational resources. To maximize learning, these sessions were hands-on with two teachers at each computer. Each teacher received instruction in, and actually used, the most popular tools available on the Internet: email (they were given temporary accounts at NASA LaRC), anonymous ftp and archie, gopher and veronica, mosaic, and telnet. Teachers participated in hands-on workshops to learn about these programs, but were also given time during the two-week session to explore on their own and to find resources on the Net that specifically met their needs. In order to ensure that Internet access continues after their return to the classroom, aIl teachers who did not have them also applied for Learning Link accounts (from WHRO, the local public television station) and Virginia Pen accounts (from the Department of Education of Virginia), both of which allow textbased access to Internet. In addition to getting exposure to and practice with Internet tools, teachers were aIso given a hands-on seminar (and also given practice time) on ClarisWorks, an integrated word processing, spreadsheet, database, and paint package. The technology sessions (and TEI as a whole) were enthusiastically received by both new and more experienced teachers as extremely helpful in improving their ability to use technology in developing lesson pIans.

Lambert, Lynn↗
DOE OSTI2024

Fracture Characterization Via AI‐Assisted Analysis of Temperature Logs

Abstract Fractures control fluid flow, mass transport, and heat transfer in a geothermal reservoir. This makes accurate characterization of fracture networks a prerequisite for optimal design and control of a reservoir's exploitation. We develop a deep‐learning procedure to identify fracture locations via interpretation of temporally and spatially continuous downhole temperature measurements. A long short‐term memory fully convolutional network (LSTM‐FCN) is used both to capture long‐term dependencies in sequential temperature data and to distill local features around fractures. A wellbore and fractured‐reservoir thermal model is established to generate temperature data for network training. The trained LSTM‐FCN exhibits a unique ability to detect multiple fractures intersecting a borehole. We use the LSTM‐FCN algorithm to evaluate the effectiveness of different‐stage wellbore temperature measurements on fracture detection in a complex fractured system. Our experiments reveal that the use of various‐stage temperature information as an input feature set improves the robustness of fracture detection to noise interference. This study indicates the practical feasibility of obtaining accurate fracture‐network reconstructions from temperature signals, at reasonable computational cost.

Yang, Xiaoyu↗
DOE OSTI2024

Overview of RFID Applications Utilizing Neural Networks

As Radio Frequency Identification (RFID) methods continue to evolve to higher levels of complexity, one form of machine learning is making its appearance. The use of Neural Networks (NN) in the RFID field is steadily increasing, and in the fields of localization and activity recognition, promising results are being shown from a variety of research. RFID applications fall primarily under two types of problems including regression and classification. We analyze RIFD localization techniques which fall under regression, and activity recognition which falls under classification. Many works don’t classify themselves as activity recognition methods, but because they fall under the classification category, we still consider them as activity recognition techniques. This research overviews the Neural Network models in the localization field based on whether they can perform independently of the environment in which they were tested. For activity recognition and accessory fields, the major methods involve tag-based and tag-free approaches. In conclusion, after the models are surveyed, a comparison study is given to examine what may be the cause for increased accuracy between different Neural Network models.

42 ENGINEERING↗
NASA NTRS2015

Experimental Products Development Team (EPDT) Supporting New AWIPS : Capabilities - Part 2

In 2012, the Experimental Products Development Team (EPDT) was formed within NASA's Short-term Prediction Research and Transition (SPoRT) Center to create training for development of plug‐ins to extend the National Weather Service (NWS) Advanced Weather Interactive Processing System (AWIPS) version 2. The broader atmospheric science community had a need for AWIPS II development training being created at SPoRT and EPDT was expanded to include other groups who were looking for training. Since the expansion of the group occurred, EPDT has provided AWIPS II development training to over thirty participants spanning a wide variety of groups such as NWS Systems Engineering Center, NWS Meteorological Development Laboratory, and several NOAA Cooperative Institutes. Participants within EPDT solidify their learning experience through hands‐on learning and by participating in a "code-sprint" in which they troubleshoot existing and develop plug‐ins. The hands‐on learning workshop is instructor lead with participants completing exercises within the AWIPS II Development Environment. During the code sprints EPDT groups work on projects important to the community and have worked on various plug‐ins such as an RGB image recipe creation tool, and an mPing (crowd sourced precipitation type reporting system) ingest and display. EPDT has developed a well‐defined training regime which prepares participants to fully develop plug‐ins for the extendible AWIPS II architecture from ingest to the display of new data. SPoRT has hosted 2 learning workshops and 1 code sprint over the last two years, and continues to build and shape the EPDT group based on feedback from previous workshops. The presentation will provide an overview of EPDT current and future activities, and best practices developed within EPDT.

Burks, Jason E.↗
NASA NTRS2015

Orion Launch Abort System Performance on Exploration Flight Test 1

This paper will present an overview of the flight test objectives and performance of the Orion Launch Abort System during Exploration Flight Test-1. Exploration Flight Test-1, the first flight test of the Orion spacecraft, was managed and led by the Orion prime contractor, Lockheed Martin, and launched atop a United Launch Alliance Delta IV Heavy rocket. This flight test was a two-orbit, high-apogee, high-energy entry, low-inclination test mission used to validate and test systems critical to crew safety. This test included the first flight test of the Launch Abort System preforming Orion nominal flight mission critical objectives. NASA is currently designing and testing the Orion Multi-Purpose Crew Vehicle (MPCV). Orion will serve as NASA's new exploration vehicle to carry astronauts to deep space destinations and safely return them to earth. The Orion spacecraft is composed of four main elements: the Launch Abort System, the Crew Module, the Service Module, and the Spacecraft Adapter (Fig. 1). The Launch Abort System (LAS) provides two functions; during nominal launches, the LAS provides protection for the Crew Module from atmospheric loads and heating during first stage flight and during emergencies provides a reliable abort capability for aborts that occur within the atmosphere. The Orion Launch Abort System (LAS) consists of an Abort Motor to provide the abort separation from the Launch Vehicle, an Attitude Control Motor to provide attitude and rate control, and a Jettison Motor for crew module to LAS separation (Fig. 2). The jettison motor is used during a nominal launch to separate the LAS from the Launch Vehicle (LV) early in the flight of the second stage when it is no longer needed for aborts and at the end of an LAS abort sequence to enable deployment of the crew module's Landing Recovery System. The LAS also provides a Boost Protective Cover fairing that shields the crew module from debris and the aero-thermal environment during ascent. Although the Orion Program has tested a number of the critical systems of the Orion spacecraft on the ground, the launch environment cannot be replicated completely on Earth. A number of flight tests have been conducted and are planned to demonstrate the performance and enable certification of the Orion Spacecraft. Exploration Flight Test 1, the first flight test of the Orion spacecraft, was successfully flown on December 5, 2014 from Cape Canaveral Air Force Station's Space Launch Complex 37. Orion's first flight was a two-orbit, high-apogee, high-energy entry, low-inclination test mission used to validate and test systems critical to crew safety, such as heat shield performance, separation events, avionics and software performance, attitude control and guidance, parachute deployment and recovery operations. One of the key separation events tested during this flight was the nominal jettison of the LAS. Data from this flight will be used to verify the function of the jettison motor to separate the Launch Abort System from the crew module so it can continue on with the mission. The LAS nominal jettison event on Exploration Flight Test 1 occurred at six minutes and twenty seconds after liftoff (See Fig. 3). The abort motor and attitude control motors were inert for Exploration Flight Test 1, since the mission did not require abort capabilities. A suite of developmental flight instrumentation was included on the flight test to provide data on spacecraft subsystems and separation events. This paper will focus on the flight test objectives and performance of the LAS during ascent and nominal jettison. Selected LAS subsystem flight test data will be presented and discussed in the paper. Exploration Flight Test -1 will provide critical data that will enable engineering to improve Orion's design and reduce risk for the astronauts it will protect as NASA continues to move forward on its human journey to Mars. The lessons learned from Exploration Flight Test 1 and the other Flight Test Vehicles will certainly contribute to the vehicle architecture of a human-rated space launch vehicle.

McCauley, R.↗
NASA NTRS2018

Apollo Missions to the Lunar Surface

Six Apollo missions to the Moon, from 1969-1972, enabled astronauts to collect and bring lunar rocks and materials from the lunar surface to Earth. Apollo lunar samples are curated by NASA Astromaterials at the NASA Johnson Space Center in Houston, TX. Samples continue to be studied and provide clues about our early Solar System. Learn more and view collected samples at: https://curator.jsc.nasa.gov/lunar.

Graff, Paige V.↗
NASA NTRS2004

Preliminary results of the autonomous sciencecraft experiment

The Autonomous Sciencecraft Experiment (ASE) will operate onboard the Earth Orbiter 1 mission 2004. The ASE software uses onboard continuous planning, robust task and goal-based execution, and onboard machine learning and patter recognition to radically increase science return by enabling intelligent downlink selection and autnomous retargeting. In this paper we will discuss how these AI technologies are synergistically integrated in multi-layer control architecture to enable a virtual spacecraft science agent.

Rabideau, Gregg↗
NASA NTRS2004

Preliminary results of the autonomous sciencecraft experiment

The Autonomous Sciencecraft Experiment (ASE) will operate onboard the Earth Orbiter 1 mission in 2003. The ASE software uses onboard continuous planning, robust task and goal-based execution, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting. In this paper we discuss how these AI technologies are synergistically integrated in multilayer control architecture to enable a virtual spacecraft science agent.

Rabideau, Gregg↗
NASA NTRS2023

Remote Plant Food Production Capability X-Hab

As human space exploration expands to long-duration missions, plant production capability serves as a solution to reduce the mass of consumables necessary to support human metabolic requirements and promote positive crew psychological health. A capability to remotely grow plants for a long-duration space mission could allow crops to be produced before a crew arrives at a staged habitat and could enable plant health to be maintained through remote operation with minimal local crew support. The University of Colorado Boulder was awarded a grant in the summer of 2012 to build a system capable of remote plant production as part of the NASA and National Space Grant Foundation eXploration Habitat (X-Hab) 2013 Academic Innovation Challenge. This report is presented as a review of the Robotic Gardening System that was designed and developed to meet the requirements outlined in the challenge. Each subsystem of the Robotic Gardening System is presented in detail. Included in this report are the design methodologies used, lessons learned throughout the design and development processes, and options or ideas for continued development and future upgrades. In addition to being a review of the Robotic Gardening System and its subsystems, this document is meant to aid with the continued development of this system in conjunction with other projects, such as team member Heather Hava’s NASA Space Technology Research Fellowship (NSTRF) work, X-Hab 2013-14 Plants Anywhere: Growing Plants in Free Habitat Spaces, and Dr. Nikolaus Correll’s NASA Early Career Professionals Fellowship.

Daniel Zukowski↗
NASA NTRS1992

Kennedy Space Center's NASA/Contractor Team-Centered Total Quality Management Seminar: Results, methods, and lessons learned

It is apparent to everyone associated with the Nation's aeronautics and space programs that the challenge of continuous improvement can be reasonably addressed only if NASA and its contractors act together in a fully integrated and cooperative manner that transcends the traditional boundaries of proprietary interest. It is, however, one thing to assent to the need for such integration and cooperation; it is quite another thing to undertake the hard tasks of turning such a need into action. Whatever else total quality management is, it is fundamentally a team-centered and team-driven process of continuous improvement. The introduction of total quality management at KSC, therefore, has given the Center a special opportunity to translate the need for closer integration and cooperation among all its organizations into specific initiatives. One such initiative that NASA and its contractors have undertaken at KSC is a NASA/Contractor team-centered Total Quality Management Seminar. It is this seminar which is the subject of this paper. The specific purposes of this paper are to describe the following: Background, development, and evolution of Kennedy Space Center's Total Quality Management Seminar; Special characteristics of the seminar; Content of the seminar; Meaning and utility of a team-centered design for TQM training; Results of the seminar; Use that one KSC contractor, EG&G Florida, Inc. has made of the seminar in its Total Quality Management initiative; and Lessons learned.

Kinlaw, Dennis C.↗
DOE OSTI2026

Leveraging machine learning to enhance aerosol classification using Single-Particle Mass Spectrometry

Advancing automated classification of atmospheric aerosols from Single-Particle Mass Spectrometry (SPMS) data remains challenging due to overlapping ion signatures, compositional diversity, and limited labeled data. This study evaluates supervised and semi-supervised learning frameworks to enhance aerosol identification by jointly leveraging labeled and unlabeled spectra. Four models were compared: a supervised Support Vector Machine (SVM), a self-training SVM, a stacked autoencoder classifier, and a stacked autoencoder trained using a temporal-ensembling Mean Teacher approach. All models achieved high and stable accuracies (90.0 %–91.1 %), surpassing previous results on the same dataset (87 %) and matching the performance of state-of-the-art deep learning methods. Despite small global metric differences (≤ 1 %), semi-supervised variants yielded up to 5 %–10 % improvements for compositionally rare particle types – such as soot (0.77 % of spectra, F1-score: 0.93–0.97) and hazelnut pollen (0.98 % of spectra, F1-score: 0.97–1.00) – equating to roughly ∼ 187 additional correctly classified spectra. These gains are scientifically significant, as such rare particles exert disproportionate influence on radiative absorption and ice nucleation processes; their improved detection reduces modeled uncertainties in aerosol absorption optical depth and mixed-phase cloud ice nucleation rates. The models' residual misclassifications (≈ 9 %) largely arise from true spectral overlap among chemically adjacent species (e.g., Na- vs. K-feldspar, coated vs. uncoated feldspars), reflecting physical compositional continuity rather than algorithmic error. Collectively, these findings demonstrate that leveraging unlabeled data to learn robust spectral representations and refine classification enhances both fidelity and interpretability, bridging data-driven analysis with aerosol–climate process understanding.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2021

Guidance and Lessons Learned from COVID-19 for Human-Subjects Research

The goal of this effort was to identify innovative strategies, approaches, and methods that have enabled researchers to continue doing meaningful human subjects research during the COVID-19 pandemic. Many organizations and recent literature primarily focus on efforts for future research in a post-COVID environment. Additionally, there have been no systematic efforts across the agency or broader human factors community to coordinate or share strategies. Therefore, the focus was on acquiring knowledge gained by researchers from experiences during COVID that could be valuable to continue human-subjects research under existing restrictions.

Coronavirus Disease 2019↗
DOE OSTI2025

Integrating Analytical Solutions and U-Net Model for Predicting Groundwater Contaminant Plumes in Pump-and-Treat Systems

Pump-and-treat (P&T) is a common technique for groundwater remediation involving the extraction and treatment of contaminated water above ground. Optimizing the design and operation of the P&T well network is essential for maximizing the system’s effectiveness and efficiency. However, this optimization often necessitates many model evaluations, leading to computationally demanding tasks. This study introduces a novel approach that integrates analytical solutions for groundwater dynamics with the U-Net (Ronneberger et al., 2015) deep learning framework to predict groundwater contaminant plume migration under dynamic pumping conditions. By incorporating the Thiem equation (Thiem, 1906) into the input preprocessing, the U-Net model transforms sparse well data into a continuous spatial field that captures the hydraulic impacts of pumping activities. This integration enables the model to leverage both deep learning capabilities and classical physics-based groundwater theories, enhancing prediction accuracy and computational efficiency. These advancements can facilitate rapid, large-scale evaluations of P&T optimization simulations, allowing for timely and effective decision-making in well placement and system management. We demonstrate the model's robust performance across both simplified transient 2D models and a more complex 3D heterogeneous site model at the 200 West P&T facility at the Hanford Site. The U-Net-based model offers substantial computational advantages, reducing simulation times significantly compared to full physics-based models and providing a powerful tool for rapid site evaluation and P&T system optimization, such as evaluating alternative P&T well network designs. Our findings highlight the potential of advanced machine learning models to significantly enhance the efficiency and sustainability of groundwater remediation efforts, offering a novel application of U-Net architecture in environmental science.

Pump-and-treat↗
NASA NTRS1990

Model-free distributed learning

Model-free learning for synchronous and asynchronous quasi-static networks is presented. The network weights are continuously perturbed, while the time-varying performance index is measured and correlated with the perturbation signals; the correlation output determines the changes in the weights. The perturbation may be either via noise sources or orthogonal signals. The invariance to detailed network structure mitigates large variability between supposedly identical networks as well as implementation defects. This local, regular, and completely distributed mechanism requires no central control and involves only a few global signals. Thus it allows for integrated on-chip learning in large analog and optical networks.

Dembo, Amir↗
NASA NTRS2014

Fifteen Years of Chandra Operation: Scientific Highlights and Lessons Learned

NASA's Chandra X-Ray Observatory, designed for three years of operation with a goal of five years is now entering its 15-th year of operation. Thanks to its superb angular resolution, the Observatory continues to yield new and exciting results, many of which were totally unanticipated prior to launch. We will review some scientific highlights and present "lessons learned" from the experience of operating this great observatory.

Weisskopf, Martin C.↗
NASA NTRS2004

Website of the Systems and Analysis Branch Supported Projects and Graph Analysis

Throughout the past few weeks I have learned a great amount of information about many interesting aspects that go on here at NASA Glenn Research Center Branch 7820. Branch 7820 is the Systems and Analysis Branch. The people involved in this Branch deal with in a nutshell the analysis of propulsion systems for Earth to orbit and space transportation systems. The first project that I had worked on was helping my mentor learn more about lunar geography and the most recommended way to maintain communication for our future lunar missions. During this time I studied the craters of the moon, especially the South Pole, to provide her with information so that she can make decisions. I also researched to provide her with contact information on those people who are specialized in lunar geography so that she may talk to them to find out more in depth information. Most of my time spent here has been helping to develop a comprehensive explanation and background of the different projects our Branch has supported. When I first came to NASA Glenn and started working with the 7820 Branch there website had many holes that needed to be filled in. I have spent numerous weeks researching information about topics such as Project Prometheus itself and one of its components Jupiter s Icy Moons Orbiter (JIMO). I have also done a large amount of research on propulsion systems and how different kinds work. I have learned many facts about Nuclear Electric Propulsion (NEP) all the way to Nuclear Thermal Propulsion (NTP) systems. I will continue to do this until all the holes are filled and find out about Global Integrated Design Environment (GLIDE) and Next Generation Launch technology (NGLT). Since most of my job was providing information to go onto a website I has to learn how to put my information into a HTML format. I had no previous knowledge on how to do that kind of task and had to study how to do it and am now able to create a document in HTML format. There has been reorganizing done here at NASA Glenn and our Branch was moved to another building. Therefore, our library had to be moved with us. I spent time helping to put together the boxes, pack the library, and label them accordingly. This was not an easy task but was an experience in itself. I was able to see old posters that NASA had produced about different space missions and look at Russian map of the US and books on space missions. I was also about to see what was in the library in terms of reference material helped because now I can make use of the information for my research on the website. Throughout my internship my mentor will provide me with graphs to analyze and recreate so that she may use them to her advantage. I will learn from every piece of data that comes my way. Later, I will study and analysis gravity-loss for Earth departure trajectories. Since I haven't done that yet I cannot really describe what 1 will learn or what exactly the project entails. The whole experience has been great and I have no doubt that it will exceed every expectation previously thought.

Kellerman, Corinne↗
DOE OSTI2026

Explainable physics-based constraints on reinforcement learning for accelerator optimization

We present a reinforcement learning (RL) framework for optimizing particle accelerator experiments that builds explainable physics-based constraints on agent behavior. The goal is to increase transparency and trust by letting users verify that the agent’s decision-making process incorporates suitable physics. Our algorithm uses a learnable surrogate function for physical observables, such as energy, and uses them to fine-tune how actions are chosen. This surrogate can be represented by a neural network or by an interpretable sparse dictionary model. We test our algorithm on a range of particle accelerator optimization environments designed to emulate the Continuous Electron Beam Accelerator Facility at Jefferson Lab. By examining the mathematical form of the learned constraint function, we are able to confirm the agent has learned to use the established physics of each environment. In addition, we find that the introduction of a physics-based surrogate enables our RL algorithms to reliably converge for difficult high-dimensional accelerator optimization environments.

explainability↗
DOE OSTI2024

The role of AI in detecting and mitigating human errors in safety-critical industries: A review

For safety-critical industries, human error (HE) presents continual risks to system productivity, reliability and safety. Artificial intelligence (AI) and machine learning (ML) methods have emerged as promising approaches to understand, categorize and mitigate the risk of HE in safety-critical industries. Furthermore, this review offers an examination of the current landscape regarding the utilization of AI/ML with regards to HE in safety-critical industries, categorizing literature into descriptive modeling, predictive modeling, prescriptive modeling, and generative modeling techniques. Additionally, the review aims to provide insights regarding themes in literature, challenges, and future research directions. Findings of the review suggest that AI/ML methods can prove useful in addressing the HE problem across safety-critical industries.

42 ENGINEERING↗