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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 1,459 records · Page 81

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NASA NTRS1987

Status of indium phosphide solar cell development at Spire

On-going development of indium phosphide solar cells for space applications is presented. The development is being carried out with a view towards both high conversion efficiency and simplicity of manufacture. The cell designs comprise the ion-implanted cell, the indium tin oxide top contact cell, and the epitaxial cell grown by metal organic chemical vapor deposition. Modelling data on the limit to the efficiency are presented and comparison is made to measured performance data.

Spitzer, M. B.↗

NASA NTRS1986

Periodicity in marine extinction events

The periodicity of extinction events is examined in detail. In particular, the temporal distribution of specific, identifiable extinction events is analyzed. The nature and limitations of the data base on the global fossil record is discussed in order to establish limits of resolution in statistical analyses. Peaks in extinction intensity which appear to differ significantly from background levels are considered, and new analyses of the temporal distribution of these peaks are presented. Finally, some possible causes of periodicity and of interdependence among extinction events over the last quarter billion years of earth history are examined.

Sepkoski, J. John, Jr.↗

NASA NTRS1990

Evolution of an Intelligent Information Fusion System

Consideration is given to the hardware and software needed to manage the enormous amount and complexity of data that the next generation of space-borne sensors will provide. An anthology is presented illustrating the evolution of artificial intelligence, science data processing, and management from the 1960s to the near future. Problems and limitations of technologies, data structures, data standards, and conceptual thinking are addressed. The development of an end-to-end Intelligent Information Fusion System that embodies knowledge of the user's domain-specific goals is proposed.

Campbell, William J.↗

NASA NTRS1992

Comparison of column water vapor measurements using downward-looking near-infrared and infrared imaging systems and upward-looking microwave radiometers

Remote soundings of precipitable water vapor from three systems are compared with each other and with ground truth from radiosondes. Ancillary data from a mesoscale network of surface observing stations and from wind-profiling radars are also used in the analysis. The three remote-sounding techniques are: (a) a reflectance technique using spectral data collected by the Airborne Visible-Infrared Imaging Spectrometer; (b) an emission technique using Visible-Infrared Spin Scan Radiometer Atmospheric Sounder (VAS) data acquired from the NOAA's GOES; and (c) a microwave technique using data from a limited network of three ground-based dual-channel microwave radiometers. The data were taken over the Front Range of eastern Colorado on 22-23 March 1990. The generally small differences between the three types of remote-sounding measurements are consistent with the horizontal and temporal resolutions of the instruments. The microwave and optical reflectance measurements agreed to within 0.1 cm; comparisons of the microwave data with radiosondes were also either as good or explainable. The largest differences between the VAS and the microwave radiometer at Elbert were between 0.4 and 0.5 cm and appear to be due to variable terrain within the satellite footprint.

Gao, Bo-Cai↗

NASA NTRS1995

Data collection in IMS: It's not as easy as it looks

Data collection in Ion Mobility Spectrometry (IMS) is not as easy an endeavor as it appears. Despite the advent of high speed personal computers and fast analog-to-digital converters (ADC's), care must be taken to ensure that reliable data are obtained in a timely fashion. This is especially true in hyphenated techniques, e.g. GC-IMS, where the amount of data increases dramatically when gas chromatography-ion mobility spectrometry (GC-IMS) data is being collected. Using the Graseby GC-IMS, with a gate repetition rate of 33 Hz, it is theoretically possible to collect 33 spectra per second. This collection rate is not realistically obtained due to a number of factors. Among these factors are inaccuracy of the timing signal from the IMS, the necessity to store the data, disk input/output limitations, disk operating system limitations, and program overhead. Taking these factors into account, we have achieved a data collection rate of 20 spectra per second. This paper will describe these problems, demonstrate the practical effects these problems present, and present methods for minimizing these effects.

Davis, Dennis M.↗

NASA NTRS1995

A satellite based telemetry link for a UAV application

The requirements for a satellite based communication facility to service the needs of the Geographical Information System (GIS) data collection community are addressed in this paper. GIS data is supplied in the form of video imagery at sub-television rates in one or more spectral bands / polarizations laced with a position correlated data stream. The limitations and vicissitudes of using a terrestrial based telecommunications link to collect GIS data are illustrated from actual mission scenarios. The expectations from a satellite based communications link by the geophysical data collection community concerning satellite architecture, operating bands, bandwidth, footprint agility, up link and down link hardware configurations on the UAV, the Mobile Control Vehicle and at the Central Command and Data Collection Facility comprise the principle issues discussed in the first section of this paper. The final section of the paper discusses satellite based communication links would have an increased volume and scope of services the GIS data collection community could make available to the GIS user community, and the price the data collection community could afford to pay for access to the communication satellite described in the paper.

Bloise, Anthony↗

NASA NTRS2001

Mixed Layer Drift Revealed by Satellite Data

For the first time we are able to derive ocean currents using the wavelet algorithm for feature tracking from two different sensors (MODIS and SeaWiFS) on different satellites. Satellite ocean color data provide an important insight to the marine biosphere because of their capability to quantify certain fundamental properties (such as phytoplankton pigment concentration, marine primary production, etc.) on a global basis. The mixed layer drift can be derived because the ocean color signal bears information from a much larger depth (10 to 30 meters) as compared with the sea surface temperature data. Although the drifter data are very limited in the study area, the comparison shows a general agreement between drifter data and satellite tracking results, especially for the cases near the Gulf Stream boundary.

Liu, Antony K.↗

NASA NTRS2011

Improve Data Mining and Knowledge Discovery Through the Use of MatLab

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(R) (MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

Shaykhian, Gholam Ali↗

NASA NTRS2011

Improve Data Mining and Knowledge Discovery through the use of MatLab

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(TradeMark)(MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

Shaykahian, Gholan Ali↗

DOE OSTI2025

FY25 status report on the addition of candidate materials in Class B Code Case

This report provides the time-dependent allowable stress calculation strategy leveraging the limited creep rupture tests data generated to support the allowable stress for 100,000 hours in American Society of Mechanical Engineers (ASME) Boiler and Pressure Vessel Code (BPVC), Section II, Part D. A variable confidence index procedure to extrapolate material properties to support 500,000 hours design life is discussed. Time-dependent allowable stresses for Class B component design and analysis are presented for Grade 1 and Grade 2 of Alloy 625. The presented data extrapolation and allowable stress calculation method will support new material addition using limited creep rupture data in the new ASME Boiler and BPVC, Section III, Division 5, Class B rules.

Part D↗

DOE OSTI2026

Web-based Preprocessing and Visualization of 3D FIB Tomography Data for Nuclear Fuel Characterization

Three-dimensional (3D) focused ion beam (FIB) tomography enables reconstruction of internal nuclear fuel features that can't be fully evaluated through surface imaging alone. This capability supports characterization of fuel constituents and defects under thermal and irradiation conditions relevant to microreactor development. However, large tomography datasets can create data-handling, loading, and visualization challenges, especially when image-stack preparation and file conversion must be completed with separate tools. The Computational Ultraspatial Tomography Toolkit for High-Resolution Object Analysis Tools (CUTTRHOAT) is an open-source web application being developed to display FIB tomography datasets available through the Nuclear Research Data System (NRDS). The current alpha version requires prepared HDF5 datasets and has limited integrated data-preparation capabilities. This project improves CUTTHROAT by adding dataset-folder selection, automatic input detection, dataset scanning, missing-slice identification, blank-slice insertion, and image-stack-to-HDF5 conversion. Two applications will be compared: the baseline CUTTHROAT alpha workflow and the updated application containing the integrated data-handling and preprocessing functions. Evaluation will consider dataset detection accuracy, conversion success, loading time, rendering responsiveness, application stability, and user interaction. Preliminary results demonstrate successful loading of existing HDF5 files and converted image stacks, while testing also identified performance reductions caused by excessive blank-slice generation. The updated workflow reduces reliance on external preparation tools and supports more direct movement from image stacks to color-code 3D visualization. Future work includes refining missing-slice handling, integrating additional preprocessing functions, like a denoising feature, parsing TIFF metadata for automatic voxel scaling, and adding manual X, Y, and Z voxel-spacing inputs for PNG and JPEG.

36 - MATERIALS SCIENCE↗

DOE OSTI2021

Knowledge extraction and transfer in data-driven fracture mechanics

Significance Data-driven approaches have launched a new paradigm in scientific research that is bound to have an impact on all disciplines of science and engineering. However, at this juncture, the exploration of data-driven techniques in the century-old field of fracture mechanics is highly limited, and there are key challenges including accurate and intelligent knowledge extraction and transfer in a data-limited regime. Here, we propose a framework for data-driven knowledge extraction in fracture mechanics with rigorous accuracy assessment which employs active learning for optimizing data usage and for data-driven knowledge transfer that allows efficient treatment of three-dimensional fracture problems based on two-dimensional solutions.

Liu, Xing↗

NASA NTRS2000

Effect of Medium Symmetries in Limiting the Number of Parameters Estimated with Polarimetric Interferometry

The addition of interferometric backscattering pairs to the conventional polarimetric SAR data over forests and other vegetated areas increases the dimensionality of the data space, in principle enabling the estimation of a larger number of vegetation parameters. Without regard to the sensitivity of these data to vegetation scattering parameters, this paper poses the question: Will increasing the data channels as such result in a one-to-one increase in the number of parameters that can be estimated, or do vegetation and data properties inherently limit that number otherwise? In this paper, the complete polarimetric interferometric covariance matrix is considered and various symmetry properties of the scattering medium are used to study whether any of the correlation pairs can be eliminated. The number of independent pairs has direct consequences in their utility in parameter estimation schemes, since the maximum number of parameters that can be estimated cannot exceed the number of unique measurements. The independent components of the polarimetric interferometric SAR (POL/INSAR) data are derived for media with reflection, rotation, and azimuth symmetries, which are often encountered in vegetated surfaces. Similar derivations have been carried out before for simple polarimetry, i.e., zero baseline. This paper extends those to the interferometric case of general nonzero baselines. It is shown that depending on the type of symmetries present, the number of independent available measurements that can be used to estimate medium parameters will vary. In particular, whereas in the general case there are 27 mathematically independent measurements possible from a polarimetric interferometer, this number can be reduced to 15, 9, and 6 if the medium has reflection, rotation, or azimuthal symmetries, respectively. The results can be used in several ways in the interpretation of SAR data and the development of parameter estimation schemes, which will be discussed at the presentation. Recent POL/INSAR data from the JPL AIRSAR over a forested area will be used to demonstrate the results of this derivation. This work was performed by the Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, under contract from the National Aeronautics and Space Administration.

Moghaddam, Mahta↗

DOE OSTI2023

ANN-based ground motion model for Turkey using stochastic simulation of earthquakes

SUMMARY Turkey is characterized by a high level of seismic activity attributed to its complex tectonic structure. The country has a dense network to record earthquake ground motions; however, to study previous earthquakes and to account for potential future ones, ground motion simulations are required. Ground motion simulation techniques offer an alternative means of generating region-specific time-series data for locations with limited seismic networks or regions with seismic data gaps, facilitating the study of potential catastrophic earthquakes. In this research, a local ground motion model (GMM) for Turkey is developed using region-specific simulated records, thus constructing a homogeneous data set. The simulations employ the stochastic finite-fault approach and utilize validated input-model parameters in distinct regions, namely Afyon, Erzincan, Duzce, Istanbul and Van. To overcome the limitations of linear regression-based models, artificial neural network is used to establish the form of equations and coefficients. The predictive input parameters encompass fault mechanism (FM), focal depth (FD), moment magnitude (Mw), Joyner and Boore distance (RJB) and average shear wave velocity in the top 30 m (Vs30). The data set comprises 7359 records with Mw ranging between 5.0 and 7.5 and RJB ranging from 0 to 272 km. The results are presented in terms of spectral ordinates within the period range of 0.03–2.0 s, as well as peak ground acceleration and peak ground velocity. The quantification of the GMM uncertainty is achieved through the analysis of residuals, enabling insights into inter- and intra-event uncertainties. The simulation results and the effectiveness of the model are verified by comparing the predicted values of ground motion parameters with the observed values recorded during previous events in the region. The results demonstrate the efficacy of the proposed model in simulating physical phenomena.

Karimzadeh, Shaghayegh (ORCID:0000000337531676)↗

NASA NTRS2015

Advanced Background Subtraction Applied to Aeroacoustic Wind Tunnel Testing

An advanced form of background subtraction is presented and applied to aeroacoustic wind tunnel data. A variant of this method has seen use in other fields such as climatology and medical imaging. The technique, based on an eigenvalue decomposition of the background noise cross-spectral matrix, is robust against situations where isolated background auto-spectral levels are measured to be higher than levels of combined source and background signals. It also provides an alternate estimate of the cross-spectrum, which previously might have poor definition for low signal-to-noise ratio measurements. Simulated results indicate similar performance to conventional background subtraction when the subtracted spectra are weaker than the true contaminating background levels. Superior performance is observed when the subtracted spectra are stronger than the true contaminating background levels. Experimental results show limited success in recovering signal behavior for data where conventional background subtraction fails. They also demonstrate the new subtraction technique's ability to maintain a proper coherence relationship in the modified cross-spectral matrix. Beam-forming and de-convolution results indicate the method can successfully separate sources. Results also show a reduced need for the use of diagonal removal in phased array processing, at least for the limited data sets considered.

Bahr, Christopher J.↗

DOE OSTI2025

Breaking the barrier of human-annotated training data for machine learning-aided plant research using aerial imagery

Machine learning (ML) can accelerate biological research. However, the adoption of such tools to facilitate phenotyping based on sensor data has been limited by (i) the need for a large amount of human-annotated training data for each context in which the tool is used and (ii) phenotypes varying across contexts defined in terms of genetics and environment. This is a major bottleneck because acquiring training data is generally costly and time-consuming. This study demonstrates how a ML approach can address these challenges by minimizing the amount of human supervision needed for tool building. A case study was performed to compare ML approaches that examine images collected by an uncrewed aerial vehicle to determine the presence/absence of panicles (i.e. “heading”) across thousands of field plots containing genetically diverse breeding populations of 2 Miscanthus species. Automated analysis of aerial imagery enabled the identification of heading approximately 9 times faster than in-field visual inspection by humans. Leveraging an Efficiently Supervised Generative Adversarial Network (ESGAN) learning strategy reduced the requirement for human-annotated data by 1 to 2 orders of magnitude compared to traditional, fully supervised learning approaches. The ESGAN model learned the salient features of the data set by using thousands of unlabeled images to inform the discriminative ability of a classifier so that it required minimal human-labeled training data. This method can accelerate the phenotyping of heading date as a measure of flowering time in Miscanthus across diverse contexts (e.g. in multistate trials) and opens avenues to promote the broad adoption of ML tools.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

pixelvar79/ESGAN-Flowering-Detection-paper

Varela, Sebastian↗

NASA NTRS2007

Cardiovascular Adaptations to Long Duration Head Down Tilt Bed Rest

Orthostatic hypotension is a recognized risk for crewmembers returning from space. Numerous cardiovascular mechanisms have been proposed to account for this problem including vascular and cardiac dysfunction. We studied arterial and cardiac function in 6-degree head-down tilt bed rest, which is the most widely accepted ground-based analog of spaceflight. Eleven subjects are included in this study (8 men and 3 women). Data analysis was limited to the first 49 days, and compared to pre-bed rest baseline data. Using ultrasound, data was collected on arterial diameters and flows at baseline and during reactive hyperemia and following administration of nitroglycerin. Echocardiography was used to acquire information regarding systolic and diastolic function as well as ventricular mass and diameter. Plasma volumes were significantly decreased by 7 days of bed rest and stayed down through 49 days. There were no differences in reactive hyperemic response in the arm at any time point. However, the hyperemic response in the leg was significantly increased at day 49. Arterial responses to nitroglycerin did not change over the duration of bed rest (day effect) in either the arm or leg, but there was a significant difference between the arm and the leg responses. There was a marked decrease in anterior tibial intimal-medial thickness at days 21, 35 and 49. Several cardiac functional parameters including IVRT, Mitral e-wave, ejection time, velocity of circumferential shortening and myocardial performance index were significantly changed following 49 days of bed rest. These data show that some cardiovascular measures change during bed rest, while others do not. Further study is needed to determine if these measures can provide any insight into the effects of bed rest, or spaceflight, on human cardiovascular performance.

Platts, Steven H.↗