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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 NTRS1992

Aerosols and polar stratospheric clouds measurements during the EASOE campaign

Preliminary results of observations performed using two different lidar systems during the EASOE (European Arctic Stratospheric Ozone Experiment), which has taken place in the winter of 1991-1992 in the northern hemisphere lattitude regions, are presented. The first system is a ground based multiwavelength lidar intended to perform measurements of the ozone vertical distribution in the 5 km to 40 km altitude range. It was located in Sodankyla (67 degrees N, 27 degrees E) as part of the ELSA experiment. The objectives of the ELSA cooperative project is to study the relation between polar stratospheric cloud events and ozone depletion with high vertical resolution and temporal continuity, and the evolution of the ozone distribution in relation to the position of the polar vortex. The second system is an airborne backscatter lidar (Leandre) which allows for the study of the 3-D structure and the optical properties of polar stratospheric clouds. The Leandre instrument is a dual-polarization lidar system, emitting at 532 nm, which allows for the determination of the type of clouds observed, according to the usual classification of polar stratospheric clouds. More than 60 hours of flight were performed in Dec. 1991, and Jan. and Feb. 1992 in Kiruna, Sweden. The operation of the Leandre instrument has led to the observation of the short scale variability of the Pinatubo volcanic cloud in the high latitude regions and to several episodes of polar stratospheric clouds. Preliminary analysis of the data is presented.

Haner, D.↗
DOE OSTI2020

A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics

A general agreement in psycholinguistics claims that syntax and meaning are unified precisely and very quickly during online sentence processing. Although several theories have advanced arguments regarding the neurocomputational bases of this phenomenon, we argue that these theories could potentially benefit by including neurophysiological data concerning cortical dynamics constraints in brain tissue. In addition, some theories promote the integration of complex optimization methods in neural tissue. In this paper we attempt to fill these gaps introducing a computational model inspired in the dynamics of cortical tissue. In our modeling approach, proximal afferent dendrites produce stochastic cellular activations, while distal dendritic branches–on the other hand–contribute independently to somatic depolarization by means of dendritic spikes, and finally, prediction failures produce massive firing events preventing formation of sparse distributed representations. The model presented in this paper combines semantic and coarse-grained syntactic constraints for each word in a sentence context until grammatically related word function discrimination emerges spontaneously by the sole correlation of lexical information from different sources without applying complex optimization methods. By means of support vector machine techniques, we show that the sparse activation features returned by our approach are well suited—bootstrapping from the features returned by Word Embedding mechanisms—to accomplish grammatical function classification of individual words in a sentence. In this way we develop a biologically guided computational explanation for linguistically relevant unification processes in cortex which connects psycholinguistics to neurobiological accounts of language. We also claim that the computational hypotheses established in this research could foster future work on biologically-inspired learning algorithms for natural language processing applications.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1973

Apollo 16 rocks - Classification and petrogenetic model

The Apollo 16 rocks include cataclastic anorthosites, two varieties of unequilibrated breccia, two varieties of partly to fully equilibrated breccia, and a sequence of partially melted breccias. The latter, which dominate the Apollo 16 collection, include glass, divitrified glass, mesostasis-olivine-plagioclase rock, mesostasis-rich basalt, basalt, and poikilitic rocks. All sequence members contain vesicles and relics of plagioclase, olivine, pink spinel, and lithic fragments. Their equilibrated matrices define a series from glass, to a plagioclase-olivine-mesostasis assemblage displaying spherulitic and skeletal shaped crystals, through a plagioclase-pyroxene-olivine assemblage displaying euhedral shaped crystals. Such data suggest that the sequence lithologies were derived from breccias or soils that were partially melted in an impact event.

Warner, J. L.↗
DOE OSTI2026

Protection System Validation with Machine Learning Anomaly Classification

A poster for the Early Career Poster Session. Power system protection devices have transitioned over the past few decades from mechanical to analog devices, then to solid state and finally digital. Relays and their associated critical network of equipment have significantly increased in complexity. Even internally, relays have gained significant intricacy, with relatively simple overcurrent or differential functions now being assisted by a myriad of other functions. This is necessary as the grid becomes more complex, but it brings increased difficulty in monitoring and upkeep. Misoperation caused by improper relay settings or malicious actions is a constant challenge faced by all utilities. These improper settings can be difficult to identify and may require exhaustive post-mortem analysis, typically after a major outage event has already occurred. A mechanism is needed for monitoring the behavior of protection systems to validate that they act and perform as expected. This work presents a concept for a machine learning (ML) system capable of validating the performance of protection systems by classifying anomalous events and characterizing protection system responses based solely on available current and voltage measurements. As a first step in its development, an experimental dataset is generated, and a random forest model is implemented with high accuracy in distinguishing four power system scenarios.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Power quality disturbances diagnosis: A 2D densely connected convolutional network framework

The fast and accurate diagnosis of power quality disturbances (PQD) aids in avoiding shutdowns and unnecessary procedures, concerning electric energy distribution systems. As such, a number of techniques have been tested and applied in order to reach this objective. Majority of the techniques applied are two-step based. On the first step, power quality disturbances features are extracted. Second step, considering features extracted, disturbance classification is implemented. Recently, relevant literature has presented data-driven signal processing-based approaches, as deep convolutional neural networks (DCNN), which can implement both processing steps while providing automated recognition of patterns and outliers in data. However, not considered by state-of-art, power quality disturbances are evolving in nature, while all possible regularities might not be represented in the dataset. In this work a 2 Dimension Densely Connected Convolutional Network (2D-DenseNet) framework is presented. Further, a case study with synthetic disturbance events are analyzed. Easy-to-implement formulation, built on the 2D-DenseNet, without hard-to-design parameters, highlight potential aspects for real-life implementation.

42 ENGINEERING↗
DOE OSTI2021

Anomaly Detection, Localization and Classification using Drifting Synchrophasor Data Streams

With ongoing automation and digitization of the electric power system, several Phasor Measurement Units(PMUs) have been deployed for monitoring and control. PMU data can have multiple anomalies, and many of the researchers in the past have concentrated on training machine/deep learning algorithms offline for anomaly detection over PMU data (i.e., not in real time). These machine/deep learning algorithms, when trained offline on a sample rather than a population of the dataset, fail to consider the dynamic behavior of the power grid in real-time, resulting in low accuracy. Considering the dynamic behavior of the power grid (e.g., change in load, generation, distributed energy resources (DERs) switching, network, controls), the definition of data anomalies varies in time and requires online training. A fundamental challenge is to enable online (i.e., real-time) training of machine/deep learning algorithms for anomaly detection over streaming PMU data. While machine/deep learning is often desirable to manage data streams, training a deep learning algorithm over streaming PMU data is nontrivial due to changes in data statistics caused by dynamic streaming data. This paper proposes PMUNET: a novel device-level deep learning-based data-driven approach for anomaly detection, localization, and classification over streaming PMU data, using online learning and multivariate data-drift detection algorithm .Two variants of PMUNET, Dynamic data Change Driven Learning (DCDL) and Continuity Driven Learning (CDL), are proposed and compared. DCDL aims to train the deep learning algorithm whenever the definition of anomaly changes due to the power grid dynamics. On the other hand, CDL continuously trains the deep learning algorithm over the PMU data-stream. The experimental results verify that DCDL outperforms CDL and other efficient anomaly detection methods over multiple events such as faults and load/ generator/capacitor/DERs variations/switching for IEEE 14 and 39 Bus test system as well as real PMU industrial data. The result verifies that DCDL variant of PMUNET improves over existing approach with a gain of 2% - 10% in terms of accuracy, false-positive rate, and false-negative rate.

adversarial deep learning↗
NASA NTRS2010

Recent Results on SNRs and PWNe from the Fermi Large Area Telescope

Topics include: Fermi LAT Collaboration groups; galactic results from LAT; a GeV, wide-field instrument; the 1FGL catalog, the Fermi LAT 1FGL source catalog, unidentified gamma-ray sources; variability in 1FGL sources; curvature in 1FGL sources; spectral-variability classification; pulsars and their wind nebulae; gamma-ray pulsars and MSPs; GeV PWN search; Crab pulsar and nebula; Vela X nebular of Vela pulsar; MSH 15-52; supernova remnants, resolved GeV sources, galactic transients, LAT unassociated transient detections; gamma rays from a nova; V407 Cyngi - a symbiotic nova; V407 Cygni: a variable star; and March 11 - a nova. Summary slides include pulsars everywhere, blazars, LAT as an electron detector, cosmic ray spectrum, the Large Area Telescope, the Fermi Observatory, LAT sensitivity with time, candidate gamma-ray events, on-orbit energy calibration and rate, a 1 year sky map, LAT automated science processing, reported GeV flares, early activity and spectacular flare, gamma-ray transients near the galactic plane , two early unassociated transients, counter part search - Fermi J0910-5404; counterpart search 3EG J0903-3531, and a new LAT transient - J1057-6027.

Hays, Elizabeth↗
NASA NTRS2017

A Model-Based, Bayesian Solution for Characterization of Complex Damage Scenarios in Aerospace Composite Structures

Ultrasonic damage detection and characterization is commonly used in nondestructive evaluation (NDE) of aerospace composite components. In recent years there has been an increased development of guided wave based methods. In real materials and structures, these dispersive waves result in complicated behavior in the presence of complex damage scenarios. Model-based characterization methods utilize accurate three dimensional finite element models (FEMs) of guided wave interaction with realistic damage scenarios to aid in defect identification and classification. This work describes an inverse solution for realistic composite damage characterization by comparing the wavenumber-frequency spectra of experimental and simulated ultrasonic inspections. The composite laminate material properties are first verified through a Bayesian solution (Markov chain Monte Carlo), enabling uncertainty quantification surrounding the characterization. A study is undertaken to assess the efficacy of the proposed damage model and comparative metrics between the experimental and simulated output. The FEM is then parameterized with a damage model capable of describing the typical complex damage created by impact events in composites. The damage is characterized through a transdimensional Markov chain Monte Carlo solution, enabling a flexible damage model capable of adapting to the complex damage geometry investigated here. The posterior probability distributions of the individual delamination petals as well as the overall envelope of the damage site are determined.

H. Reed↗
NASA NTRS2000

Three Conservation Applications of Astronaut Photographs of Earth: Tidal Flat Loss (Japan), Elephant Impacts on Vegetation (Botswana), and Seagrass and Mangrove Monitoring (Australia)

NASA photographs taken from low Earth orbit can provide information relevant to conservation biology. This data source is now more accessible due to improvements in digitizing technology, Internet file transfer, and availability of image processing software. We present three examples of conservation-related projects that benefited from using orbital photographs. (1) A time series of photographs from the Space Shuttle showing wetland conversion in Japan was used as a tool for communicating about the impacts of tidal flat loss. Real-time communication with astronauts about a newsworthy event resulted in acquiring current imagery. These images and the availability of other high resolution digital images from NASA provided timely public information on the observed changes. (2) A Space Shuttle photograph of Chobe National Park in Botswana was digitally classified and analyzed to identify the locations of elephant-impacted woodland. Field validation later confirmed that areas identified on the image showed evidence of elephant impacts. (3) A summary map from intensive field surveys of seagrasses in Shoalwater Bay, Australia was used as reference data for a supervised classification of a digitized photograph taken from orbit. The classification was able to distinguish seagrasses, sediments and mangroves with accuracy approximating that in studies using other satellite remote sensing data. Orbital photographs are in the public domain and the database of nearly 400,000 photographs from the late 1960s to the present is available at a single searchable location on the Internet. These photographs can be used by conservation biologists for general information about the landscape and in quantitative applications.

Lulla, Kamlesh P.↗
DOE OSTI2025

A Novel Machine Learning Workflow to Classify Mobile Home Parks at Scale

Understanding the built environment is essential to the overall study of population dynamics, grid infrastructure, emergency response, among others. In the United States there are multiple classifications for buildings within the built environment such as residential, signifying family homes while commercial buildings consist of apartments or larger structures which are multi-purpose. While there is a high level of understanding of where these aforementioned structures are located, there is a third class of structures, mobile home parks (MHP) which have been under-represented in the literature despite there being an estimated 2.7 million of them within the United States. Research has shown that individuals who reside in MHP are at higher risk to extreme events due to their location and structural integrity of residence. Attention must now turn to identifying MHP at scale to help first responders and policy makers understand where these at risk populations reside. To address for this gap, we develop a novel methodology to infer MHP at scale based off morphologies derived at a building level. Here, we show that across 3 million buildings in 6 states within the United States it is possible to identify MHP with 83% accuracy. This novel approach to identify MHP from other structures within the built environment using a machine learning approach provides a new tool to leverage in relation to helping at-risk populations.

Stipek, Clinton [Oak Ridge National Laboratory (OR↗
NASA NTRS2006

Onboard Classifiers for Science Event Detection on a Remote Sensing Spacecraft

Typically, data collected by a spacecraft is downlinked to Earth and pre-processed before any analysis is performed. We have developed classifiers that can be used onboard a spacecraft to identify high priority data for downlink to Earth, providing a method for maximizing the use of a potentially bandwidth limited downlink channel. Onboard analysis can also enable rapid reaction to dynamic events, such as flooding, volcanic eruptions or sea ice break-up. Four classifiers were developed to identify cryosphere events using hyperspectral images. These classifiers include a manually constructed classifier, a Support Vector Machine (SVM), a Decision Tree and a classifier derived by searching over combinations of thresholded band ratios. Each of the classifiers was designed to run in the computationally constrained operating environment of the spacecraft. A set of scenes was hand-labeled to provide training and testing data. Performance results on the test data indicate that the SVM and manual classifiers outperformed the Decision Tree and band-ratio classifiers with the SVM yielding slightly better classifications than the manual classifier.

classification↗
DOE OSTI2024

Pulse shape discrimination technique for diffuse supernova neutrino background search with JUNO

Pulse shape discrimination (PSD) is widely used in particle and nuclear physics. Specifically in liquid scintillator detectors, PSD facilitates the classification of different particle types based on their energy deposition patterns. This technique is particularly valuable for studies of the diffuse supernova neutrino background (DSNB), nucleon decay, and dark matter searches. This paper presents a detailed investigation of the PSD technique, applied in the DSNB search performed with the Jiangmen Underground Neutrino Observatory (JUNO). Instead of using conventional cut-and-count methods, we employ methods based on boosted decision trees and neural networks and compare their capability to distinguish the DSNB signals from the atmospheric neutrino neutral-current background events. The two methods demonstrate comparable performance, resulting in a 50–80% improvement in signal efficiency compared to a previous study performed for JUNO (An et al. [JUNO] in J Phys G 43(3):030401, 2016). Moreover, we study the dependence of the PSD performance on the visible energy and final state composition of the events and find a significant dependence on the presence/absence of 11 C. Finally, we evaluate the impact of the detector effects (photon propagation, PMT dark noise, and waveform reconstruction) on the PSD performance.

FOS: Physical sciences↗
NASA NTRS1997

Sustainable Biosphere Initiative Project

The goal of the Advanced Technology in Ecological Sciences project is to gain broad participation within the environmental scientific community in developing a research agenda addressing the development and refinement of technologies instrumental to research that responds to these challenges (e.g. global climate change, unsustainable resource use, and threats to biological diversity). The following activities have been completed: (1) A listserve 'eco-tech was set up to serve as a clearinghouse of information about activities and events relating to advanced technologies; (2) A series of conference calls were organized on specific topics including data visualization and spatial analysis, and remote sensing; and (3) Two meetings were organized at the 19% ESA Annual Meeting in Providence, Rhode Island. Topics covered included concerns about tool and data sharing; interest in expanded development of ground-based remote sensing technologies for monitoring; issues involved in training for using new technologies and increasing data streams, and- associated implications of data processing capabilities; questions about how to develop appropriate standards (i.e. surface morphology classification standards) that facilitate the exchange and comparison of analytical results; and some thoughts about remote sensing platforms and vehicles.

Source record↗
NASA NTRSDate not supplied

Spectral Characterization of Spacecraft Materials used in Hypervelocity Impact Testing

The increasing number of successfully deployed space missions have resulted in an increased density of man-made objects positioned in orbital domains near Earth. With this steady accumulation of objects in space, it is becoming more imperative to characterize spacecraft materials, which may ultimately be contributors to the orbital debris population. In order to ascertain the potential damage from orbital debris, a laboratory hypervelocity impact test was conducted using a 56-kg modern spacecraft representative satellite (DebriSat) to simulate a catastrophic fragmentation event in low Earth orbit. In an effort to identify unique, material-specific spectroscopic markers, a select number of the spacecraft materials used to construct DebriSat were analyzed using reflectance spectroscopy as a characterization technique for assessment on material type according to optical features. Spectral measurements of DebriSat materials analyzed prior to the laboratory impact are presented in this paper. These data provide a spectral characterization baseline for modern-day spacecraft materials in their pristine conditions and are compared to each other to distinguish spectra of materials belonging to different classifications with an effort of grouping them using color index. The ongoing efforts to classify materials utilizing their reflectance spectroscopic fingerprint are discussed in this study

Jacqueline A. Reyes↗
NASA NTRS1986

Results from the solar maximum mission

The major results from SMM (Solar Max Mission) are presented as they relate to the understanding of the energy release and particle transportation processes that led to the high energy X-ray aspects of solar flares. Evidence is reviewed for a 152- to 158-day periodicity in various aspects of solar activity including the rate of occurrence of hard X-ray and gamma-ray flares. The statistical properties of over 7000 hard X-ray flares detected with the Hard X-Ray Burst Spectrometer are presented including the spectrum of peak rates and the distribution of the photo number spectrum. A flare classification scheme is used to divide flares into three different types. Type A flares have purely thermal, compact sources with very steep hard X-ray spectra. Type B flares are impulsive bursts which show double footpoints in hard X-rays, and soft-hard-soft spectral evolution. Type C flares have gradually varying hard X-ray and microwave fluxes from high altitudes and show hardening of the X-ray spectrum through the peak and on the decay. SSM data are presented for examples of Type B and Type C events. New results are presented showing coincident hard X rays, O V, and UV continuum observations in Type B events with a time resolution of 128 ms. The subsecond variations in the hard X-ray flux during 10% of the stronger events are discussed and the fastest observed variation in a time of 20 ms is presented. The properties of Type C flares are presented as determined primarily from the non-imaged hard X-ray and microwave spectral data. A model based on the association of Type C flares and coronal mass ejections is presented to explain many of the characteristics of these gradual flares.

Dennis, B. R.↗
NASA NTRS1995

Pilot-Induced Oscillations and Human Dynamic Behavior

This is an in-depth survey and study of pilot-induced oscillations (PIO's) as interactions between human pilot and vehicle dynamics; it includes a broad and comprehensive theory of PIO's. A historical perspective provides examples of the diversity of PIO's in terms of control axes and oscillation frequencies. The constituents involved in PIO phenomena, including effective aircraft dynamics, human pilot dynamic behavior patterns, and triggering precursor events, are examined in detail as the structural elements interacting to produce severe pilot-induced oscillations. The great diversity of human pilot response patterns, excessive lags and/or inappropriate gain in effective aircraft dynamics, and transitions in either the human or effective aircraft dynamics are among the key sources implicated as factors in severe PIO's. The great variety of interactions which may result in severe PIO's is illustrated by examples drawn from famous PIO's. These are generalized under a pilot-behavior-theory-based set of categories proposed as a classification scheme pertinent to a theory of PIO's. Finally, a series of interim prescriptions to avoid PIO is provided.

McRuer, Duane T.↗
NASA NTRS1993

Ten years in the library: new data confirm paleontological patterns

A comparison is made between compilations of times of origination and extinction of fossil marine animal families published in 1982 and 1992. As a result of ten years of library research, half of the information in the compendia has changed: families have been added and deleted, low-resolution stratigraphic data been improved, and intervals of origination and extinction have been altered. Despite these changes, apparent macroevolutionary patterns for the entire marine fauna have remained constant. Diversity curves compiled from the two data bases are very similar, with a goodness-of-fit of 99%; the principal difference is that the 1992 curve averages 13% higher than the older curve. Both numbers and percentages of origination and extinction also match well, with fits ranging from 83% to 95%. All major events of radiation and extinction are identical. Therefore, errors in large paleontological data bases and arbitrariness of included taxa are not necessarily impediments to the analysis of pattern in the fossil record, so long as the data are sufficiently numerous.

NASA Discipline Number 52-40↗
DOE OSTI2023

Reference data, predictors, and probability grids for forest degradation classes in three sites in the Brazilian Amazon

Forest degradation by fires and selective logging is widespread in the Amazon region. We implemented a gradient boosted classification modeling framework to classify intact, logged, and burned forests at three Amazonian sites: Feliz Natal Municipality and Xingu Indigenous Territory in Mato Grosso State, and Saracá-Taquera National Forest in Pará State. We used forest degradation history from Landsat time-series as reference data and textural metrics derived from PlanetScope images as predictors. Textural metrics were computed using the Gray-Level Co-Occurrence Matrix (GLCM) textural technique. Included in the attached zip file are ten files: - a shapefile containing the reference data (fire and selective logging polygons and year of event) for each site; - a multiband tif file containing the 8 GLCM metrics used as predictors (Mean, Variance, Homogeneity, Contrast, Dissimilarity, Entropy, Angular Second Moment, Correlation) at the original PlanetScope resolution (3.125m) for each site; - a multiband tif file containing the 72 aggregated GLCM metrics used as predictors (Mean, Variance, Homogeneity, Contrast, Dissimilarity, Entropy, Angular Second Moment, and Correlation aggregated using the mean, first quartile, third quartile, maximum, median, minimum, root mean square, standard deviation, and skewness statistics) at 562m resolution for each site; - a multiband tif file containing the 3 probability grids for either intact, logged, or burned forests at the aggregation resolution (562m) for each site.

54 ENVIRONMENTAL SCIENCES↗