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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

Walking to Olympus: An EVA Chronology

Spacewalkers enjoy a view of Earth once reserved for Apollo, Zeus, and other denizens of Mt. Olympus. During humanity's first extravehicular activity (EVA), Alexei Leonov floated above Gibraltar, the rock ancient seafarers saw as the gateway to the great unknown Atlantic. The symbolism was clear, Leonov stepped past a new Gibraltar when he stepped into space. More than 32 years and 154 EVAs later, Jerry Linenger conducted an EVA with Vladimir Tsibliyev as part of International Space Station Phase 1. They floated together above Gibraltar. Today the symbolism has new meaning: humanity is starting to think of stepping out of Earth orbit, space travel's new Gibraltar, and perhaps obtaining a new olympian view, a close-up look at Olympus Mons on Mars. Walking to Olympus: An EVA Chronology chronicles the 154 EVAs conducted from March 1965 to April 1997. It is intended to make clear the crucial role played by EVA in the history of spaceflight, as well as to chronicle the large body of EVA "lessons learned." Russia and the U.S. define EVA differently. Russian cosmonauts are said to perform EVA any time they are in vacuum in a space suit. A U.S. astronaut must have at least his head outside his spacecraft before he is said to perform an EVA. The difference is based in differing spacecraft design philoso- phies. Russian and Soviet spacecraft have always had a specialized airlock through which the EVA cosmonaut egressed, leaving the main habitable volume of the spacecraft pressurized. The U.S. Gemini and Apollo vehicles, on the other hand, depressurized their entire habitable volume for egress. In this document, we apply the Russian definition to Russian EVAS, and the U.S. definition to U.S. EVAS. Thus, for example, Gemini 4 Command Pilot James McDivitt does not share the honor of being first American spacewalker with Ed White, even though he was suited and in vacuum when White stepped out into space. Non-EVA spaceflights are listed in the chronology to provide context and to display the large num- ber of flights in which EVA played a role. This approach also makes apparent significant EVA gaps, for example, the U.S. gap between 1985 and 1991 following the Challenger accident. This NASA History Monograph is an edited extract from an extensive EVA Chronology and Reference Book being produced by the EVA Project Office, NASA Johnson Space Center, Houston, Texas. The larger work will be published as part of the NASA Formal Series in 1998. The authors gratefully acknowledge the assistance rendered by Max Ary, Ashot Bakunts, Gert-Jan Bartelds, Frank Cepollina, Andrew Chaikin, Phillip Clark, Richard Fullerton, Steven Glenn, Linda Godwin, Jennifer Green, Greg Harris, Clifford Hess, Jeffrey Hoffman, David Homan, Steven Hopkins, Nicholas Johnson, Eric Jones, Neville Kidger, Joseph Kosmo, Alexei Lebedev, Mark Lee, James LeBlanc, Dmitri Leshchenskii, Jerry Linenger, Igor Lissov, James McBarron, Clay McCullough, Joseph McMann, Story Musgrave, Dennis Newkirk, James Oberg, Joel Powell, Lee Saegesser, Andy Salmon, Glen Swanson, Joseph Tatarewicz, Kathy Thornton, Chris Vandenberg, Charles Vick, Bert Vis, David Woods, Mike Wright, John Young, and Keith Zimmerman. Special thanks to Laurie Buchanan, John Charles, Janet Kovacevich, Joseph Loftus, Sue McDonald, Martha Munies, Colleen Rapp, and Jerry Ross. Any errors remain the responsibility of the authors.

Portree, David S. F.↗
NASA NTRS2004

Traveling Wave Tube (TVT) RF Power Combining Demonstration for use in the Jupiter Icy Moons Orbiter (JIMO)

The Jupiter Icy Moons Orbiter (JIMO) is set to launch between the years 2012 and 2015. It will possibly utilize a nuclear reactor power source and ion engines as it travels to the moons of Jupiter. The nuclear reactor will produce hundreds of kilowatts of power for propulsion, communication and various scientific instruments. Hence, the RF amplification devices aboard will be able to operate at a higher power level and data rate. The initial plan for the communications system is for an output of 1000 watts of RF power, a data rate of at least 10 megabits a second, and a frequency of 32 GHz. A higher data rate would be ideal to fully utilize the instruments aboard JIMO. At NASA Glenn, one of our roles in the JIMO project is to demonstrate RF power combining using multiple traveling wave tubes (TWT). In order for the power of separate TWT s to be combined, the RF output waves from each must be in-phase and have the same amplitude. Since different tubes act differently, we had to characterize each tube using a Network Analyzer. We took frequency sweeps and power sweeps to characterize each tube to ensure that they will behave similarly under the same conditions. The 200 watt Dornier tubes had been optimized to run at a lower power level (120 watts) for their extensive use in the ACTS program, so we also had to experiment with adjusting the voltage settings on several internal components (helix, anode, collector) of the tubes to reach the full 200 watt potential. from the ACTS program. Phase shifters and power attenuators were placed in the waveguide circuit at the inputs to the tubes so that adjustments could be made individually to match them exactly. A magic tee was used to route and combine the amplified electromagnetic RF waves on the tube output side. The demonstration of 200 watts of combined power was successful with efficiencies greater than 90% over a 500 MHz bandwidth. The next step will be to demonstrate the use of three amplifiers using two magic tees by adding a 200 watt Dornier tube to the Varian and Logimetrics combined setup for a total of 400 watts. After that we will use two 200 watt Dorniers for 400 watts and eventually four 200 watt Dornier tubes to demonstrate 800 watts. After demonstrating the success of power combining, we will need to verify the integrity of a modulated signal sent through the combined tubes. The purpose will be to see what effects separating and recombining will have on the modulated signal and also what effect it will have on combining efficiency. A Bit Error Rate (BER) will be determined by a Bit Error Rate Tester (BERT) by comparing the random information it transmits to what it receives back. The process began with two 100 watt tubes, a Varian and a Logimetrics, salvaged

Downey, Joseph A.↗
NASA NTRSDate not supplied

Natural Language Processing Analysis of Notices to Airmen for Air Traffic Management Optimization

With new emerging technologies in the field of NLP, we explore their applications to digitize and analyze heritage Air Traffic Management (ATM) documents for planning and optimizing airspace operations. Specifically, this research focuses on harvesting semi-structured or un-structured information contained in Notices to Airmen (NOTAMs). Using NLP and other advanced data analytics, we will construct a data-driven framework which facilitates finding language patterns and the use of pretrained language models for classification and extraction of useful airspace constraints and restrictions. These may lead to tools that assist airspace users in understanding the constraints more efficiently, contributing to better route planning and safer execution. This paper explores three workflows entailing different NLP tasks. First, unsupervised techniques like word embedding and topic modeling are used for pattern finding and document classification. Second, a dataset is created by extracting information from the semi-structured NOTAM format as metadata for categorizing, visualizing, and extracting key entities driving NOTAM content. Third, modern pre-built deep learning based transformer models such as BERT, RoBERTa, and XLNet are evaluated on the question answering task, an even more robust approach to information extraction, as well as their respective fine-tuning tasks. In this work we include various performance metrics for the trained models to evaluate both accuracy and precision and we show that the models can be generalized for their respective tasks. The research work developed shows promise in uncovering trends in digital NOTAMs in the NAS and also offers a new framework for digitizing and inferring insights from free-form legacy NOTAMs, that are yet to be digitized.

Natural Language Processing↗
NASA NTRSDate not supplied

Natural Language Processing (NLP) Analysis of NOTAMs for Air Traffic Management Optimization

With new emerging technologies in the field of NLP, we explore their applications to digitize and analyze heritage Air Traffic Management (ATM) documents for planning and optimizing airspace operations. Specifically, this research focuses on harvesting semi-structured or un-structured information contained in Notices to Airmen (NOTAMs). Using NLP and other advanced data analytics, we will construct a data-driven framework which facilitates finding language patterns and the use of pretrained language models for classification and extraction of useful airspace constraints and restrictions. These may lead to tools that assist airspace users in understanding the constraints more efficiently, contributing to better route planning and safer execution. This paper explores three workflows entailing different NLP tasks. First, unsupervised techniques like word embedding and topic modeling are used for pattern finding and document classification. Second, a dataset is created by extracting information from the semi-structured NOTAM format as metadata for categorizing, visualizing, and extracting key entities driving NOTAM content. Third, modern pre-built deep learning based transformer models such as BERT, RoBERTa, and XLNet are evaluated on the question answering task, an even more robust approach to information extraction, as well as their respective fine-tuning tasks. In this work we include various performance metrics for the trained models to evaluate both accuracy and precision and we show that the models can be generalized for their respective tasks. The research work developed shows promise in uncovering trends in digital NOTAMs in the NAS and also offers a new framework for digitizing and inferring insights from free-form legacy NOTAMs, that are yet to be digitized. Video is an mp4 download, with a play time of 9 min 35 secs.

Natural Language Processing↗
NASA NTRSDate not supplied

A Brief Introduction to AI/ML Applications of Air Traffic Management Data at NASA Ames

This presentation will give a brief overview of several AI/ML This presentation will give a brief overview of several AI/ML projects that NASA Ames interns are exploring in partnership with NASA Aeronautic Research Institute (NARI) and the FAA. NASA is interested in Natural Language Processing (NLP) of various legacy text and speech data within air traffic management e.g., Notices To Airmen (NOTAMs), Letters of Agreement (LoAs), Standard Operating Procedures (SOPs), and Air Traffic Control Center audio briefings. Since our focus is on applying state of the art AI/ML tools to legacy air traffic management data, we first showcase the different data sources of interest followed by a brief introduction to the techniques and language models used. We present some exciting preliminary results on each topic including both unsupervised learning techniques (e.g., clustering) and other modern language models (e.g., BERT) that help extract useful information from these data sources that are interpretable by both man and machine.

Air Traffic Management↗
NASA NTRS2020

TopiQAL: Topic-aware Question Answering using Scalable Domain-specific Supercomputers

We all have questions. About today's temperature, scores of our favorite baseball team, the Universe, and about vaccine for COVID-19. Life, physical, and natural scientists have been trying to find answers to various topics using scientific methods and experiments, while computer scientists have built language models as a tiny step towards automatically answering all of these questions across domains given a little bit of context. In this paper, we propose an architecture using state-of-the-art Natural Language Processing language models namely Topic Models and Bidirectional Encoder Representations from Transformers (BERT) that can transparently and automatically retrieve articles of relevance to questions across domains, and fetch answers to topical questions related to COVID-19 current and historical medical research literature. We demonstrate the benefits of using domain-specific supercomputers like Tensor Processing Units (TPUs), residing on cloud-based infrastructure, using which we could achieve significant gains in training and inference times, also with very minimal cost.

Penberthy, Scott↗
NASA NTRSDate not supplied

What Went Wrong: A Survey of Wildfire UAS Mishaps through Named Entity Recognition

Increasingly, unmanned aircraft systems (UAS) are being applied to wildfire incidents for tasks such as mapping, aerial ignition, and delivery. As a result, incident reporting systems for wildfires are beginning to accumulate data related to UAS mishaps in wildfire response. In this research, we apply state-of-the-art natural language processing (NLP) techniques to develop a custom Named Entity Recognition (NER) model which extracts a Failure Modes and Effects Analysis (FMEA)-style survey of wildfire UAS mishaps reported in SAFECOM. The custom NER model is built by fine-tuning an existing (BERT) model, resulting in a generalizable NER model that can extract engineering relevant entities including failure modes, causes, effects, control processes, and recommendations from any failure-relevant text. Similar mishaps are clustered and reported as single rows within the FMEA. For each cluster, frequency, severity, and overall risk are computed. The methodology can be applied as part of a broader safety management system to track trends in mishaps and discover knowledge that can be utilized to improve safety outcomes and system performance.

Machine Learning↗
NASA NTRSDate not supplied

What Went Wrong: A Survey of Wildfire UAS Mishaps through Named Entity Recognition

Increasingly, unmanned aircraft systems (UAS) are being applied to wildfire incidents for tasks such as mapping, aerial ignition, and delivery. As a result, aviation incident reporting systems for wildfires are beginning to accumulate data related to UAS mishaps in wildfire response. In this research, we apply state-of-the-art natural language processing (NLP) techniques to develop a custom Named Entity Recognition (NER) model which extracts entities relevant to safety analysts. The custom NER model is built by fine-tuning an existing Bidirectional Encoder Representations from Transformers (BERT) model, resulting in a generalizable NER model that can extract engineering relevant entities including failure modes, causes, effects, control processes, and recommendations from failure-relevant text. This model performs passably, with a weighted average f1 score of 0.33 across entity types, indicating more labeled training data is needed. Extracted entities are used to form a Failure Modes and Effects Analysis (FMEA)-style survey of wildfire UAS mishaps reported using the SAFECOM system. Similar mishaps are manually clustered and reported as single rows within an FMEA. Foreach cluster, we compute frequency, severity, and overall riskin accordance with FAA standards. This methodology can beapplied as part of a broader safety management system totrack trends in mishaps (e.g., likelihood, severity) and discoverknowledge (e.g., causes, effects) that can be utilized to improvesafety outcomes and system performance.

Machine Learning↗
NASA NTRSDate not supplied

What Went Wrong: A Survey of Wildfire UAS Mishaps through Named Entity Recognition

Increasingly, unmanned aircraft systems (UAS) are being applied to wildfire incidents for tasks such as mapping, aerial ignition, and delivery. As a result, incident reporting systems for wildfires are beginning to accumulate data related to UAS mishaps in wildfire response. In this research, we apply state-of-the-art natural language processing (NLP) techniques to develop a custom Named Entity Recognition (NER) model which extracts a Failure Modes and Effects Analysis (FMEA)-style survey of wildfire UAS mishaps reported in SAFECOM. The custom NER model is built by fine-tuning an existing (BERT) model, resulting in a generalizable NER model that can extract engineering relevant entities including failure modes, causes, effects, control processes, and recommendations from any failure-relevant text. Similar mishaps are clustered and reported as single rows within the FMEA. For each cluster, frequency, severity, and overall risk are computed. The methodology can be applied as part of a broader safety management system to track trends in mishaps and discover knowledge that can be utilized to improve safety outcomes and system performance.

Machine Learning↗
NASA NTRS1972

In-plane, flexural, twisting and thickness-shear coefficients for stiffness and damping of a monolayer filamentary composite, part 1

Elastic and damping analyses resulting in determinations of the various stiffnesses and associated loss tangents for the complete characterization of the elastic and damping behavior of a monofilament composite layer are presented. For the determination of the various stiffnesses, either an elementary mechanics-of-materials formulation or a more rigorous mixed-boundary-value elasticity formulation is used. The solution for the latter formulation is obtained by means of the boundary-point least-square error technique. Kimball-Lovell type damping is assumed for each of the constituent materials. For determining the loss tangents associated with the various stiffnesses, either the viscoelastic correspondence principle or an energy analysis based on the appropriate elastic stress distribution is used.

Bert, C. W.↗
NASA NTRS1972

Vibration and damping of laminated, composite-material plates including thickness-shear effects

An analytical investigation of sinusoidally forced vibration of laminated, anisotropic plates including bending-stretching coupling, thickness-shear flexibility, all three types of inertia effects, and material damping is presented. In the analysis the effects of thickness-shear deformation are considered by the use of a shear correction factor K, analogous to that used by Mindlin for homogeneous plates. Two entirely different approaches for calculating the thickness-shear factor for a laminate are presented. Numerical examples indicate that the value of K depends on the layer properties and the stacking sequence of the laminate.

Bert, C. W.↗
NASA NTRS1973

Sinusoidal response of composite-material plates with material damping.

A general forced-vibration analysis is presented for laminated anisotropic rectangular plates including material damping. The theory used is the laminated version of the Mindlin plate theory and includes thickness-shear flexibility and rotatory and coupling inertia. A solution is obtained by the Rayleigh-Ritz method, extended to include the energy dissipated and the work done by the excitation. The analysis is applied to prediction of the resonant frequencies and associated nodal patterns and damping ratios of the first five modes for a series of rectangular plates with free edges. The plates considered consist of unidirectional boron-fiber/epoxy composite material with respective fiber orientations of 0, 10, 30, 45, 60, and 90 deg.

Siu, C. C.↗
NASA NTRS1974

Evaluation of experimental methods for determining dynamic stiffness and damping of composite materials

Various methods potentially usable for determining dynamic stiffness and damping of composite materials are reviewed. Of these, the following most widely used techniques are singled out for more detailed discussion: free vibration, pulse propagation, and forced vibration response. To illustrate the usefulness and validity of dynamic property data, their application in dynamic analyses and comparison with measured structural response are described for the following composite-material structures: free-free sandwich beam with glass-epoxy facings, clamped-edge sandwich plate with similar facings, free-end sandwich conical shell with similar facings, and boron-epoxy free plate with layers arranged at various orientations.

Bert, C. W.↗
NASA NTRS1974

Analysis of longitudinal thickness-shear stiffness of a monolayer filamentary composite

The longitudinal thickness-shear behavior of a rectangular cross-section prism containing circular cross-section fibers embedded in a matrix is analyzed. The problem is formulated as a Saint-Venant flexure problem modeled by a tip-loaded cantilever composite beam and solution is obtained by the boundary-point least-squares method. It is shown that the longitudinal thickness-shear modulus is much greater than the in-plane shear modulus for a monofilament composite and therefore an independent analysis is necessary to predict the longitudinal thickness-shear behavior of the composite. Numerical results are presented in graphical form for various values of fiber volume fraction and material parameters typical of modern advanced composites.

Chang, S.↗
NASA NTRS1974

Analysis of twisting stiffness for a multifiber composite layer

The twisting stiffness of a rectangular cross section consisting of a single row of solid circular cross-section fibers embedded in a matrix is analyzed. The problem is formulated as a Dirichlet torsion problem of a multielement region and solved by the boundary-point least-squares method. Numerical results for a single-fiber square cross section compare favorably with previous relaxation-method results. New numerical results for three and five-fiber composites suggest that the torsional rigidity of a multifiber composite can be approximated from the torsional rigidities of single and three-fiber models.

Bert, C. W.↗
NASA NTRS1975

Experimental characterization of composites

The experimental characterization for composite materials is generally more complicated than for ordinary homogeneous, isotropic materials because composites behave in a much more complex fashion, due to macroscopic anisotropic effects and lamination effects. Problems concerning the static uniaxial tension test for composite materials are considered along with approaches for conducting static uniaxial compression tests and static uniaxial bending tests. Studies of static shear properties are discussed, taking into account in-plane shear, twisting shear, and thickness shear. Attention is given to static multiaxial loading, systematized experimental programs for the complete characterization of static properties, and dynamic properties.

Bert, C. W.↗