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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Cu-Al-Ni Shape Memory Single Crystal Wires with High Transformation Temperature

CN-250X is a new material with higher performance than Nickel-Titanium Shape Memory Alloy (SMA). For space mechanisms, the main disadvantage of Nickel-Titanium Shape Memory Alloy is the limited transformation temperature. The new CN-250X Nimesis alloy is a Cu-Al-Ni single crystal wire available in large quantity because of a new industrial process. The triggering of actuators made with this Cu-Al-Ni single crystal wire can range from ambient temperature to 200 C in cycling and even to 250 C in one-shot mode. Another advantage of CN-250X is a better shape recovery (8 to 10%) than Ni-Ti (6 to 7%). Nimesis is the first company able to produce this type of material with its new special industrial process. A characterization study is presented in this work, including the two main solicitation modes for this material: tensile and torsion. Different tests measure the shape recovery of Cu-Al-Ni single crystals wires during heating from room temperature to a temperature higher than temperature of end of martensitic transformation.

Hautcoeur, Alain↗
NASA NTRS2016

Transformational Autonomy and Personal Transportation: Synergies and Differences Between Cars and Planes

Highly automated cars have undergone tremendous investment and progress over the past ten years with speculation about fully-driverless cars within the foreseeable, or even near future, becoming common. If a driverless future is realized, what might be the impact on personal aviation? Would self-piloting airplanes be a relatively simple spin-off, possibly making travel by personal aircraft also commonplace? What if the technology for completely removing human drivers turns out to be further in the future rather than sooner; would such a delay suggest that transformational personal aviation is also somewhere over the horizon or can transformation be achieved with less than full automation? This paper presents a preliminary exploration of these questions by comparing the operational, functional, and implementation requirements and constraints of cars and small aircraft for on-demand mobility. In general, we predict that the mission management and perception requirements of self-piloting aircraft differ significantly from self-driving cars and requires the development of aviation specific technologies. We also predict that the highly-reliable control and system automation technology developed for conditionally and highly automated cars can have a significant beneficial effect on personal aviation, even if full automation is not immediately feasible.

Goodrich, Kenneth H.↗
NASA NTRS2017

Transformers for Lunar Extreme Environments: Ensuring Long-Term Operations in Regions of Darkness and Low Temperatures: II - Phase

This report shows how solar power could enable robotic operations in permanently shaded regions at lunar poles, to extract water ice and further produce liquid hydrogen and oxygen (LH2/LO2) propellant. The power needs are derived from an Architecture for Human Exploration of Mars based entirely on lunar propellant. The extraction of 10 metric tons of water per day (at 10% water in regolith) requires approx. 0.6 MW thermal power. Additional approx. 2 MW electric power are required to produce 7.5 metric tons of LH2/LO2 propellant per day, as needed by the architecture. To provide power to processing equipment inside Shackleton Crater, optimal locations are determined on the crater rim, from which several reflecting TransFormers (TFs) would redirect sunlight, achieving a combined period of illumination of approx. 99% of the year. A single 40-m diameter reflector could provide up to 1 MW solar power. Inflatable rigidizable tower support structures raise reflectors above ground for better solar exposure. There are trade-offs: e.g., two reflectors at ground level would provide the same combined total illumination as a single tower approx. 100-m tall. Such a TF based on a 100-m tower made with inflatable 2-m beams and 40-m diameter reflectors would be of similar dimensions as an MSL-class rover (approx. 1000 kg, 10 m(exp 3)). A TF-prospector rover combo could be designed and deployed in a Discovery-class mission searching for water. The TransFormers would be nodes of a Lunar Utilities Infrastructure that provides solar power year-round in the proximity of the pole, as well as local data transmission andintermittent direct to earth communications. This infrastructure would be instrumental infacilitating the development of a lunar economy.

fuel↗
NASA NTRS2019

NASA's Digital Transformation (DT) Initiative

NASA's Digital Transformation (DT) Initiative will dramatically enhance NASA's mission impact by reinventing mission and mission support processes, products, and capabilities, enabled by an innovation culture, digital-savvy workforce, and advanced digital technologies, building on a foundation of modern data management and IT security. This presentation, as part of a conference panel, provides an overview of NASA's DT Initiative, and describes how the Initiative supports an "antidisciplinary" future, where the greatest mission transformation opportunities will be found at the intersections and mergers of multiple disciplines.

Biegel, Bryan↗
NASA NTRS2021

Expanding the application of soil moisture monitoring systems through regression-based transformation

Relative to other geophysical variables, soil moisture (SM) estimates derived from land surface models (LSMs) and land data assimilation systems (LDAS) are difficult to transfer between platforms and applications. This difficulty stems from the highly model-dependent nature of LSM SM estimates and differences in the vertical support of discretized SM values. As a result, operational SM estimates generated by one LSM (or LDAS) cannot generally be directly applied to a hydrologic monitoring or forecast system designed around a second LSM. This lack of transferability is particularly problematic for LDAS applications, where the time, expertise, and computational resources required to generate an operational LDAS analysis cannot be practically duplicated for every LSM-specific application. Here, we develop a set of simple regression tools for translating SM estimates between LSMs and multiple LDAS analyses. Results demonstrate that simple multivariate linear regression - utilizing independent variables based on multi-layer and temporally lagged SM estimates - can significantly improve upon baseline transformation approaches using direct percentile matching. The proposed regression approaches are effective for both the LSM-to-LSM and LDAS-to-LDAS transformation of multi-layer SM percentiles. Application of this approach will expand the utility of existing, high-quality (but LSM-specific) operational sources of SM information like the NASA Soil Moisture Active Passive Level-4 Soil Moisture product.

Soil Moisture↗
NASA NTRS2021

Utilizing local phase transformation strengthening for nickel-based superalloys

Almost 75 years of research has been devoted to producing superalloys capable of higher operating temperatures in jet turbine engines, and there is an ongoing need to increase operating temperature further. Here, a new disk Nickel-base superalloy is designed to take advantage of strengthening atomic-scale dynamic complexions. This local phase transformation strengthening provides the alloy with a three times improvement in creep strength over similar disk superalloys and comparable strength to a single crystal blade alloy at 760 °C. Ultra-high-resolution chemical mapping reveals that the improvement in creep strength is a result of atomic-scale η (D024) and χ (D019) formation along superlattice stacking faults. To understand these results, the energy differences between the L12 and competing D024 and D019 stacking fault structures and their dependence on composition are computed by density functional theory. This study can help guide researchers to further optimize local phase transformation strengthening mechanisms for alloy development.

Timothy M. Smith↗
NASA NTRS2023

Microstructural Changes and Pb Mobility During the Zircon to Reidite Transformation: Implications for Planetary Impact Chronology

Impact events modify and leave behind a complex history of rock metamorphism on terrestrial planets. Evidence for an impact event may be recorded in physical changes to minerals, such as mineral deformation and formation of high P-T polymorphs, but also in the form of chemical fingerprints, such as enhanced elemental diffusion and isotopic mixing. Here we explore laboratory shock-induced physical and chemical changes to zircon and feldspar, the former of which is of interest because its trace elements abundances and isotope ratios are used extensively in geochemistry and geochronology. To this end, a granular mixture of Bishop Tuff sanidine and Kuehl Lake zircon, both with well characterized Pb isotope compositions, was prepared and then shocked via a flat plate accelerator. The peak pressure of the experiment, as calculated by the impedance matching method, was ~24 GPa although a broader range of P-T conditions is anticipated due to starting sample porosity. Unshocked and shocked materials were characterized via scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), and Raman spectroscopy. These methods show that the starting zircon material had abundant metamict regions, and the conversion of the feldspar to glass in the post-shock material. Analyses of the shocked product also yielded multiple occurrences of the high-pressure ZrSiO 4 polymorph reidite, with some domains up to 300 μm across. The possibility of U-Pb system disturbance was evaluated via laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) and secondary ion mass spectrometry (SIMS). The isotopic data reveal that disturbance of the U-Pb geochronometer in the reidite was minimal (<2% for the main U-Pb geochronometers). To better constrain the P-T conditions during the shock experiment, we complement impedance matching pressure calculations with iSALE2D impact simulations. The simulated results yield a range of P-T conditions experienced during the experiment and show that much of the sample may have reached >30 GPa, which is consistent with formation of reidite. In the recovered shocked material, we identified lamellae of reidite, some of which interlock with zircon lamellae. Reidite {112} twins were identified, which we interpret to have formed to reduce stress between the crystal structure of the host zircon and reidite. These two findings support the interpretation that shear transformation enabled the transition of zircon to reidite. The size and presence of reidite found here indicate that this phase is probably common in impact-shocked crustal rocks that experienced ~25 to ~35 GPa, especially when the target material has porosity. Additionally, shock loading of the zircon and transformation to reidite at these pressures in porous materials is unlikely to significantly disturb the U-Pb system in zircon and that the reidite inherits the primary U and Pb elemental and isotopic ratios from the zircon.

Bishop Tuff↗
NASA NTRS2024

How the INCOSE Model-Based Capability Matrix Has Steered Model-Based Systems Engineering Transformation at NASA

The National Aeronautics and Space Administration (NASA) is embarking on new, complex, and diverse missions to accomplish its scientific and exploration objectives, and it views digital transformation as a key enabler for those missions. The NASA Model-Based Systems Engineering (MBSE) Lead-ership Team (MLT) is leading the charge in the digital transformation of the systems engineering domain at NASA, and it is using the INCOSE Model-Based Capability Matrix (MBCM) as a roadmap. This paper discusses the modifications and tailoring of the INCOSE MBCM (Hale & Hoheb, 2020) for use at NASA, the process the team has taken on multiple rounds of assessment, findings to date, and work products that have been generated as a result of the assessment. The paper will also discuss findings and potential changes that should be made to the original product.

MBSE↗
NASA NTRSDate not supplied

Towards an Aviation Large Language Model by Fine-tuning and Evaluating Transformers

In the aviation domain, there are many applications for machine learning and artificial intelligence tools that utilize natural language. For example, there is a desire to know the commonalities in written safety reports such as voluntary post incidents reports or create more accurate transcripts of air traffic management conversations. Another use-case is the possibility of extracting airspace procedures and constraints currently written in documents such as Letters of Agreement (LOA) which is used as the evaluation case in this paper. These applications can benefit from the use of state-of-the-art Natural Language Processing (NLP) techniques when adapted to the language/phraseology specific to the aviation domain. This paper evaluates the viability of transferring pre-trained large language models to the aviation domain by adapting transformer based models using aviation datasets. This paper utilized two datasets to adapt a ‘Robustly Optimized Bidirectional Encoder Representations from Transformers Approach’ (RoBERTa) model and two down-stream classification tasks to assess its performance. These datasets are all built upon Letters of Agreement which are Federal Aviation Administration (FAA) documents that formalize airspace operations across the national airspace system. The first two datasets are used for the adaptation of RoBERTa to the aviation domain and were of different sizes to assess the number of documents needed to adapt to the aviation domain. They contain many examples of ‘aviation English’ using domain specific terminology and phrasing which serves as a representative basis to perform the unsupervised adaptation. The second dataset is a separate set of LOA documents with two sets of classification labels to be used for evaluation; one at the document level and one at the line level. These down-stream evaluations allowed the measurement of improvement by adapting RoBERTa. The accuracy increased by 4-6% on both tasks and the F1 score on the class of interest increased by 4-8% from the adaptation.

Air Traffic Management↗
NASA NTRSDate not supplied

Towards an Aviation Large Language Model by Fine-tuning and Evaluating Transformers

In the aviation domain, there are many applications for machine learning and artificial intelligence tools that utilize natural language. For example, there is a desire to know the commonalities in written safety reports such as voluntary post incidents reports or create more accurate transcripts of air traffic management conversations. Another use-case is the possibility of extracting airspace procedures and constraints currently written in documents such as Letters of Agreement (LOA) which is used as the evaluation case in this paper. These applications can benefit from the use of state-of-the-art Natural Language Processing (NLP) techniques when adapted to the language/phraseology specific to the aviation domain. This paper evaluates the viability of transferring pre-trained large language models to the aviation domain by adapting transformer based models using aviation datasets. This paper utilized two datasets to adapt a ‘Robustly Optimized Bidirectional Encoder Representations from Transformers Approach’ (RoBERTa) model and two down-stream classification tasks to assess its performance. These datasets are all built upon Letters of Agreement which are Federal Aviation Administration (FAA) documents that formalize airspace operations across the national airspace system. The first two datasets are used for the adaptation of RoBERTa to the aviation domain and were of different sizes to assess the number of documents needed to adapt to the aviation domain. They contain many examples of ‘aviation English’ using domain specific terminology and phrasing which serves as a representative basis to perform the unsupervised adaptation. The second dataset is a separate set of LOA documents with two sets of classification labels to be used for evaluation; one at the document level and one at the line level. These down-stream evaluations allowed the measurement of improvement by adapting RoBERTa. The accuracy increased by 4-6% on both tasks and the F1 score on the class of interest increased by 4-8% from the adaptation.

Air Traffic Management↗