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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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

Partial Automation of Requirements Tracing

Requirements Tracing on Target (RETRO) is software for after-the-fact tracing of textual requirements to support independent verification and validation of software. RETRO applies one of three user-selectable information-retrieval techniques: (1) term frequency/inverse document frequency (TF/IDF) vector retrieval, (2) TF/IDF vector retrieval with simple thesaurus, or (3) keyword extraction. One component of RETRO is the graphical user interface (GUI) for use in initiating a requirements-tracing project (a pair of artifacts to be traced to each other, such as a requirements spec and a design spec). Once the artifacts have been specified and the IR technique chosen, another component constructs a representation of the artifact elements and stores it on disk. Next, the IR technique is used to produce a first list of candidate links (potential matches between the two artifact levels). This list, encoded in Extensible Markup Language (XML), is optionally processed by a filtering component designed to make the list somewhat smaller without sacrificing accuracy. Through the GUI, the user examines a number of links and returns decisions (yes, these are links; no, these are not links). Coded in XML, these decisions are provided to a "feedback processor" component that prepares the data for the next application of the IR technique. The feedback reduces the incidence of erroneous candidate links. Unlike related prior software, RETRO does not require the user to assign keywords, and automatically builds a document index.

Hayes, Jane↗
NASA NTRS2024

Presound: UAV Diagnostic System Enabled by Vibration-Based Machine Learning

A low-weight, inexpensive small unmanned aerial system (sUAS) that takes off, performs a mission, lands, and safely stows and recharges itself has myriad future applications ranging from agricultural imaging to last-mile package delivery. Likewise, Urban Air Mobility (UAM) systems will enable people to take air taxis from point to point in cities, rapidly moving commuters long distances without concern for road traffic and congestion. Fully electric aviation systems will be cleaner and quieter than ground transport. Cities could eliminate cars and buses, and convert roads to higher capacity bike and pedestrian throughways. Yet, for sUAS as well as UAM, system reliability and assurance is a limiting factor to deploying affordable autonomous flight systems. For this bright future of aviation to be realized, aircraft must be able to autonomously and accurately self-diagnose health issues both before takeoff and during flight. The GreenSight PreSound system is designed to identify defects on aircraft through intelligent analysis of vibration. It accomplishes this by measuring structural vibrations induced by the vehicle’s own propellers, and analyzing that data using a machine learning model that determines whether a defect is present. The PreSound system is designed to require no human oversight, and to operate across a wide array of vehicles through re-training of the model for each target aircraft. PreSound has been developed and seen limited early success using data collected from the GreenSight Dreamer sUAS, a 5lb quadrotor vehicle designed for aerial imaging applications. The final detection model, trained on data with props spinning at 50% throttle, achieves excellent performance with over 99% average accuracy in detecting blade damage using a single FFT vector input. It demonstrates the ability to generalize to new types of blade damage, correctly classifying a different type of blade damage with 98% accuracy. Full test pulses were classified with 100% accuracy, and in live testing, all sets of data during blade movement were classified accurately with over 95% confidence. When trained on in-flight data, the same model achieves an average accuracy of 85% in distinguishing between undamaged and blade-damaged states in flight. The authors believe that these accuracies show significant potential of this approach to expand unmanned flight safety, with significant potential benefits in accelerating Advanced Aerial Mobility (AAM) and UAM aviation applications.

UAS↗
DOE OSTI2023

DC electric field generation and distribution in magnetized plasmas

Very large DC and AC electric fields cannot be sustained between conducting electrodes because of volume gas breakdown and/or surface field emission. However, very large potential fields are now routinely generated in plasma structures, such as laser generated wake in unmagnetized plasmas. In magnetized plasmas, large DC fields can also be sustained and controlled perpendicular to the magnetic field, but the metallic end plates limiting the plasma, terminating the magnetic field lines, and usually providing the voltage drop feed between the field lines impose severe restrictions on the maximum field. However, it is shown that very large radial DC voltage drops can be sustained by injecting waves of predetermined frequencies and wave vectors, traveling along the azimuthal direction of an axially magnetized plasma cylinder, or by injecting fast neutral particles beams along this azimuthal direction. The large conductivity along the magnetic field lines and the small conductivity between the field lines then distribute this voltage drop. Furthermore, the global power balance and control parameters of wave and beam generated large DC electric fields in magnetized plasmas are identified, described, and analyzed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Machine Learning-Based Identification of the Interface Regions for Coupling Local and Nonlocal Models

Local-nonlocal coupling approaches provide a means to combine the computational efficiency of local models and the accuracy of nonlocal models. However, the coupling process can be challenging, requiring expertise to identify the interface between local and nonlocal regions. Here, this study introduces a machine learning-based approach to automatically detect the regions in which the local and nonlocal models should be used. The method uses loading functions evaluated at grid points to decide the model selection at those points. Training of the networks is based on datasets provided by classes of loading functions for which reference coupling configurations are computed using accurate coupled solutions, where accuracy is measured in terms of the relative error between the solution to the coupling approach and the solution to the nonlocal model. We study two approaches that vary in data structure. The first, the full-domain input data approach, uses the entire load vector and outputs a complete label vector, performing a global classification. The second, a window-based approach, processes loads into windows and addresses the problem as a node-wise classification where each window's central point is classified individually. The classification problems are solved via deep learning algorithms based on convolutional neural networks. The performance of these approaches is studied on one-dimensional numerical examples using F1-scores and accuracy metrics. Notably, the windowing approach achieves an accuracy of 0.96 and an F1-score of 0.97, highlighting its potential to automate coupling processes effectively and enhance computational efficiency in material science applications.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1981

Radar investigation of asteroids

Radar investigations were conducted of selected minor planets, including: (1) observations during 1981-82 of 10 potential targets (2 Pallas, 8 Flora, 12 Victoria, 15 Eunomia, 19 Fortuna, 22 Kalliope, 132 Aethra, 219 Thusnelda, 433 Eros, and 2100 Ra-Shalom); and (2) continued analyses of observational data obtained during 1980-81 for 10 other asteroids (4 Vesta, 7 Iris, 16 Psyche, 75 Eurydike, 97 Klotho, 216 Kleopatra, 1685 Toro, 1862 Apollo, 1865 Cerberus, and 1915 Quetzalcoatl). Scientific objectives include estimation of echo strength, polarization, spectral shape, spectral bandwidth, and Doppler shift. These measurements: (1) yield estimates of target size, shape, and spin vector; (2) place constraints on topography, morphology, and composition of the planetary surface; (3) yield refined estimates of target orbital parameters; (4) reveal the presence of asteroidal satellites.

Ostro, S. J.↗
NASA NTRS2002

Wire Detection Algorithms for Navigation

In this research we addressed the problem of obstacle detection for low altitude rotorcraft flight. In particular, the problem of detecting thin wires in the presence of image clutter and noise was studied. Wires present a serious hazard to rotorcrafts. Since they are very thin, their detection early enough so that the pilot has enough time to take evasive action is difficult, as their images can be less than one or two pixels wide. Two approaches were explored for this purpose. The first approach involved a technique for sub-pixel edge detection and subsequent post processing, in order to reduce the false alarms. After reviewing the line detection literature, an algorithm for sub-pixel edge detection proposed by Steger was identified as having good potential to solve the considered task. The algorithm was tested using a set of images synthetically generated by combining real outdoor images with computer generated wire images. The performance of the algorithm was evaluated both, at the pixel and the wire levels. It was observed that the algorithm performs well, provided that the wires are not too thin (or distant) and that some post processing is performed to remove false alarms due to clutter. The second approach involved the use of an example-based learning scheme namely, Support Vector Machines. The purpose of this approach was to explore the feasibility of an example-based learning based approach for the task of detecting wires from their images. Support Vector Machines (SVMs) have emerged as a promising pattern classification tool and have been used in various applications. It was found that this approach is not suitable for very thin wires and of course, not suitable at all for sub-pixel thick wires. High dimensionality of the data as such does not present a major problem for SVMs. However it is desirable to have a large number of training examples especially for high dimensional data. The main difficulty in using SVMs (or any other example-based learning method) is the need for a very good set of positive and negative examples since the performance depends on the quality of the training set.

Kasturi, Rangachar↗
NASA NTRS2020

Evaluation of Low Cost, User-Centered Alerting Devices for the Mitigation of Flight Crew Spatial Disorientation

The National Aeronautics and Space Administration (NASA) is conducting research into technologies which have the potential to reduce flight crew Spatial Disorientation (SD). While flight deck technology has advanced rapidly over the past fifty (50) years, the reported occurrences of flight crew SD have not decreased. The Cost-Effective Devices for Alerting Research (CEDAR) effort has focused on the identification and development of low cost, user-centered alerting solutions for the purpose of mitigating the occurrence of flight crew SD. This effort seeks to evaluate both existing technologies as well as new and emerging technologies which have a viable path to implementation in commercial aviation within the next five (5) years. This research is intended to develop a proof-of-concept for real-time SD mitigation which could eventually be utilized to improve safety in future air transport operations. NASA has partnered with the United States Naval Aeromedical Research Unit in Dayton, OH (NAMRU-D) to conduct flight crew SD research utilizing their Disorientation Research Device (DRD), dubbed “The Kraken”. This high-tech simulator features never-before-seen capabilities for side-by-side commercial aviation flight crew SD research, and is capable of re-creating the forces necessary to induce SD illusions in a safe and repeatable manner. Prototype SD mitigation solutions were incorporated into this simulator for scenario-based testing and evaluation using airline pilots within a contextually representative operational environment. SD mitigation technology prototypes designed to provide haptic feedback for both alerting and guidance, as well as innovative visual and aural alerting displays were evaluated. In December 2014, as a result of analyzing eighteen (18) loss-of-control events, the Commercial Aviation Safety Team (CAST) recommended research into flight deck technologies that have potential to mitigate the problems and contributing factors that lead to flight crew loss of airplane state awareness (ASA) and conditions likely to produce SD. The aviation community (government, industry and academia) have been charged with conducting research into cost-effective, user-centered flight deck alerting systems to alert flight crews; especially for the two conditions that produced SD (sub-threshold rolls and the somatogravic illusion). Somatogravic illusions are defined as illusions in which “there is a false perception of attitude on exposure to a force vector that differs in direction and/or magnitude from the normal gravitational force”. The sub-threshold roll illusion (aka the “leans” illusion) is a false sensation of roll attitude. A prolonged roll of less than two degrees becomes physiologically imperceptible to the pilot, and can lead to an incorrect perception of aircraft orientation. The sub-threshold roll illusion forms the entry point for the “graveyard spiral”, a maneuver in which the pilot’s corrective action (based on their false perception of aircraft orientation) causes the aircraft to spiral down into the ground. In both of these SD illusions, it is when the pilot attempts to correct the aircraft’s attitude (based on a false perception of its state) that problems arise, sometimes with lethal consequences.

Ronald J Daiker↗
NASA NTRSDate not supplied

Non-Mechanical Beam Steering for NASA's Navigation Doppler LiDAR

Transparent electro-optic (EO) devices can potentially be used for efficient, high speed, and low mass non-mechanical beam steering. Transmission through EO devices will affect the wavefront quality and aberration of both the transmitted and received laser light. The consequences of wavefront distortions on coherent detection are not fully understood. Therefore, we have tested the performance of coherent detection lidar using liquid crystal polarization grating (LCPG) beam steering device. We will discuss the impact of LCPG beam steering devices on the performance of NASA’s Navigation Doppler Lidar, built for providing altitude and vector velocity data to aerial and space vehicles.

Lidar↗
DOE OSTI2020

Machine-Learning-Driven, Site-Specific Weather Forecasting for Grid-Interactive Efficient Buildings: Preprint

Emerging grid-interactive efficient buildings (GEBs) have great potential to provide much-needed demand flexibility to electric grids while fulfilling their own control targets by co-optimizing smart appliances, solar photovoltaics, electric vehicles, and energy storage at buildings. To enable the optimal operation of GEBs, site-specific weather information—such as temperature, solar irradiance, relative humidity, and wind speed—is crucial; however, this information is generally unavailable or expensive to obtain. This paper develops advanced machine learning methods to provide precise weather forecasts for individual building sites using readily available weather station data. Support vector regression and artificial neural networks have been employed to learn the spatiotemporal correlations between the weather conditions at nearby weather stations and the individual building site. The proposed site-specific weather forecasting methods have been validated using 1-year actual weather measurement data collected in the Denver metro area. Results show that the developed machine-learning-driven methods can accurately forecast the temperature at the target building site 1 hour ahead with mean absolute error less than 0.72°C and a 48% improvement over the persistence method. Site-specific weather forecasts will improve the understanding of the microclimate effect and its impact on building energy consumption. This information will drive efficiency upgrades and adjustments of building control strategies to improve energy savings and increase flexibility in building loads.

30 DIRECT ENERGY CONVERSION↗
NASA NTRSDate not supplied

Latest Progress and Results in LEO-GEO and GEO-GEO Stereo AMVs

Atmospheric motion vectors (AMVs), derived by tracking patterns, represent the winds in a layer characteristic of the pattern. AMV height (or pressure), important for applications in atmospheric research and operational meteorology, is usually assigned using observed IR brightness temperatures with a modeled atmosphere and can be inaccurate. Stereoscopic tracking provides a direct geometric height measurement of the pattern that an AMV represents. We extend our previous work with multi-angle imaging spectro–radiometer (MISR) and GOES to moderate resolution imaging spectroradiometer (MODIS) and the GOES-R series advanced baseline imager (ABI). MISR is a unique satellite instrument for stereoscopy with nine angular views along track, but its images have a narrow (380 km) swath and no thermal IR channels. MODIS provides a much wider (2330 km) swath and eight thermal IR channels that pair well with all but two ABI channels, offering a rich set of potential applications. Given the similarities between MODIS and VIIRS, our methods should also yield similar performance with VIIRS. Our methods, as enabled by advanced sensors like MODIS and ABI, require high-accuracy geographic registration in both systems but no synchronization of observations. AMVs are retrieved jointly with their heights from the disparities between triplets of ABI scenes and the paired MODIS granule. We validate our retrievals against MISR-GOES retrievals, operational GOES wind products, and by tracking clear-sky terrain. We demonstrate that the 3D-wind algorithm can produce high-quality AMV and height measurements for applications from the planetary boundary layer (PBL) to the upper troposphere, including cold-air outbreaks, wildfire smoke plumes, and hurricanes.

Atmospheric motion vectors↗
DOE OSTI2021

Enabling deeper learning on big data for materials informatics applications

Abstract The application of machine learning (ML) techniques in materials science has attracted significant attention in recent years, due to their impressive ability to efficiently extract data-driven linkages from various input materials representations to their output properties. While the application of traditional ML techniques has become quite ubiquitous, there have been limited applications of more advanced deep learning (DL) techniques, primarily because big materials datasets are relatively rare. Given the demonstrated potential and advantages of DL and the increasing availability of big materials datasets, it is attractive to go for deeper neural networks in a bid to boost model performance, but in reality, it leads to performance degradation due to the vanishing gradient problem. In this paper, we address the question of how to enable deeper learning for cases where big materials data is available. Here, we present a general deep learning framework based on Individual Residual learning (IRNet) composed of very deep neural networks that can work with any vector-based materials representation as input to build accurate property prediction models. We find that the proposed IRNet models can not only successfully alleviate the vanishing gradient problem and enable deeper learning, but also lead to significantly (up to 47%) better model accuracy as compared to plain deep neural networks and traditional ML techniques for a given input materials representation in the presence of big data.

36 MATERIALS SCIENCE↗
DOE OSTI2023

AdenPredictor: accurate prediction of the adenylation domain specificity of nonribosomal peptide biosynthetic gene clusters in microbial genomes

Microbial natural products represent a major source of bioactive compounds for drug discovery. Among these molecules, nonribosomal peptides (NRPs) represent a diverse class that include antibiotics, immunosuppressants, anticancer agents, toxins, siderophores, pigments, and cytostatics. The discovery of novel NRPs remains a laborious process because many NRPs consist of nonstandard amino acids that are assembled by nonribosomal peptide synthetases (NRPSs). Adenylation domains (A-domains) in NRPSs are responsible for selection and activation of monomers appearing in NRPs. During the past decade, several support vector machine-based algorithms have been developed for predicting the specificity of the monomers present in NRPs. These algorithms utilize physiochemical features of the amino acids present in the A-domains of NRPSs. In this article, we benchmarked the performance of various machine learning algorithms and features for predicting specificities of NRPSs and we showed that the extra trees model paired with one-hot encoding features outperforms the existing approaches. Moreover, we show that unsupervised clustering of 453560 A-domains reveals many clusters that correspond to potentially novel amino acids. While it is challenging to predict the chemical structure of these amino acids, we developed novel techniques to predict their various properties, including polarity, hydrophobicity, charge, and presence of aromatic rings, carboxyl, and hydroxyl groups.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Electrical Switching and Imaging of Antiferromagnetic Spins in Perovskite Epitaxial Thin Films

Electrical control of antiferromagnets is essential for the field of antiferromagnetic spintronics that promises ultrafast speed and terahertz dynamics. However, the large family of complex oxide antiferromagnets such as perovskites have been largely unexplored. Here, we demonstrate electrical switching and detection of Néel vector in LaFeO 3 epitaxial thin films with an epitaxy-enabled biaxial anisotropy, of which the switching behavior is understood by comparing to magnetic field controlled spin Hall magnetoresistance measurements. The electrical switching is corroborated by imaging of antiferromagnetic domains using X-ray Magnetic Linear Dichroism Photoemission Electron Microscopy. Electrical switching of 3-nm and 10-nm LaFeO 3 films was achieved over a wide temperature range of 25 to 300 K. This work offers an attractive platform for exploration of antiferromagnetic spintronics and other emerging phenomena such as potential insulating altermagnetism.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Probing nuclear structure at the Electron-Ion Collider and in ultra-peripheral nuclear collisions

Within the Color Glass Condensate framework, we demonstrate that exclusive vector meson production at high energy is sensitive to the geometric deformation of the target nucleus and subnucleon scale fluctuations. Deformation of the nucleus enhances the incoherent cross section in the small |t| region. Subnucleon scale fluctuations increase the incoherent cross section in the large |t| region. In ultra-peripheral collisions (UPCs), larger deformation leads to a wider distribution of the minimal impact parameter B min required to produce a UPC. This, together with larger incoherent cross sections for larger deformation, results in smaller extracted radii. Our results demonstrate great potential for future studies of nuclear structure in UPCs and electron-ion collisions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Real-space solution to the electronic structure problem for nearly a million electrons

We report a Kohn–Sham density functional theory calculation of a system with more than 200 000 atoms and 800 000 electrons using a real-space high-order finite-difference method to investigate the electronic structure of large spherical silicon nanoclusters. Our system of choice was a 20 nm large spherical nanocluster with 202 617 silicon atoms and 13 836 hydrogen atoms used to passivate the dangling surface bonds. To speed up the convergence of the eigenspace, we utilized Chebyshev-filtered subspace iteration, and for sparse matrix–vector multiplications, we used blockwise Hilbert space-filling curves, implemented in the PARSEC code. For this calculation, we also replaced our orthonormalization + Rayleigh–Ritz step with a generalized eigenvalue problem step. We utilized all of the 8192 nodes (458 752 processors) on the Frontera machine at the Texas Advanced Computing Center. We achieved two Chebyshev-filtered subspace iterations, yielding a good approximation of the electronic density of states. Our work pushes the limits on the capabilities of the current electronic structure solvers to nearly 106 electrons and demonstrates the potential of the real-space approach to efficiently parallelize large calculations on modern high-performance computing platforms.

Chemistry↗
DOE OSTI2024

Detecting damage in composites using volume decomposition analysis of tomographic data

Detection of damage in a single tow ceramic matrix composite specimen has been achieved using orthogonal decomposition of volumetric tomographic datasets collected at four tensile loads. This decomposition approach has been applied at two different length scales: (i) individual fibres and (ii) bulk volumes containing fibres and matrix material. Volumes were first decomposed to feature vectors, orders of magnitude smaller than the original volume they describe, and then comparisons between datasets at different load levels were made in feature vector space. The results show quantitative measurements of damage location, damage morphology and the relative growth of this damage with increased load when compared with a dataset with less or no damage. No prior knowledge of the dataset or training of algorithms is required for damage to be detected, it is only necessary that at least two datasets are available for comparison, e.g. from in situ or repeated scanning measurements. Results are generated on significantly shorter timescales when compared with previous automated approaches to tomography data processing. This approach has the potential to be applied to damage detection in a range of materials through comparisons of volumetric datasets from a range of measurement or computational techniques.

Middleton, Ceri A.↗
DOE OSTI2024

Quasi-Two-Dimensional Antiferromagnetic Spin Fluctuations in the Spin-Triplet Superconductor Candidate CeRh 2 As 2

The tetragonal heavy-fermion superconductor CeRh 2 ⁢As 2 (T c = 0.3 K) exhibits an exceptionally high critical field of 14 T for B ∥ c. It undergoes a field-driven first-order phase transition between superconducting states, potentially transitioning from spin-singlet to spin-triplet superconductivity. To further understand these superconducting states and the role of magnetism, we probe spin fluctuations in CeRh 2 ⁢As 2 using neutron scattering. Here, we find dynamic (π, π) antiferromagnetic (AFM) spin correlations with an anisotropic quasi-two-dimensional correlation volume. Our data place an upper limit of 0.31 μ B on the staggered magnetization of corresponding Néel orders at T = 0.08 K. Density functional theory calculations, treating Ce 4⁢ƒ electrons as core states, show that the AFM wave vector connects significant areas of the Fermi surface. Our findings indicate that the dominant excitations in CeRh 2 ⁢As 2 for ℏω⁢ < 1.2 meV are magnetic and suggest that superconductivity in CeRh 2⁢ As 2 is mediated by AFM spin fluctuations associated with a proximate quantum critical point.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Ferroelectricity in a semiconducting all-inorganic halide perovskite

Ferroelectric semiconductors are rare materials with both spontaneous polarizations and visible light absorptions that are promising for designing functional photoferroelectrics, such as optical switches and ferroelectric photovoltaics. The emerging halide perovskites with remarkable semiconducting properties also have the potential of being ferroelectric, yet the evidence of robust ferroelectricity in the typical three-dimensional hybrid halide perovskites has been elusive. Here, we report on the investigation of ferroelectricity in all-inorganic halide perovskites, CsGeX 3 , with bandgaps of 1.6 to 3.3 eV. Their ferroelectricity originates from the lone pair stereochemical activity in Ge (II) that promotes the ion displacement. This gives rise to their spontaneous polarizations of ~10 to 20 μC/cm 2 , evidenced by both ab initio calculations and key experiments including atomic-level ionic displacement vector mapping and ferroelectric hysteresis loop measurement. Furthermore, characteristic ferroelectric domain patterns on the well-defined CsGeBr 3 nanoplates are imaged with both piezo-response force microscopy and nonlinear optical microscopic method.

36 MATERIALS SCIENCE↗