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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 307 records · Page 17

Measuring earth orientation with the Global Positioning System

A globally distributed network of high-precision receivers which obtain data from the full Global Positioning System (GPS) configuration of 18 or more satellites may soon become an efficient and economical method for the rapid determination of short-term variations in earth orientation. A covariance analysis has been performed to evaluate the errors associated with GPS monitoring of earth orientation. Earth orientation parameters were modeled either as constants over observing windows of various lengths, or as stochastic process-noise variables. The sensitivity of earth orientation estimates to systematic errors in selected model parameters was also examined. GPS measurements appear to be highly competitive with those from other techniques, and have the potential to generate nearly continuous centimeter-level earth orientation information to aid both spacecraft navigation and the study of high-frequency earth orientation-related processes.

Freedman, Adam P.↗

A superior edge preserving filter with a systematic analysis

A new, adaptive, edge preserving filter for use in image processing is presented. It had superior performance when compared to other filters. Termed the contiguous K-average, it aggregates pixels by examining all pixels contiguous to an existing cluster and adding the pixel closest to the mean of the existing cluster. The process is iterated until K pixels were accumulated. Rather than simply compare the visual results of processing with this operator to other filters, some approaches were developed which allow quantitative evaluation of how well and filter performs. Particular attention is given to the standard deviation of noise within a feature and the stability of imagery under iterative processing. Demonstrations illustrate the performance of several filters to discriminate against noise and retain edges, the effect of filtering as a preprocessing step, and the utility of the contiguous K-average filter when used with remote sensing data.

Holladay, Kenneth W.↗

AVIRIS and TIMS data processing and distribution at the land processes distributed active archive center

The U.S. Government has initiated the Global Change Research program, a systematic study of the Earth as a complete system. NASA's contribution of the Global Change Research Program is the Earth Observing System (EOS), a series of orbital sensor platforms and an associated data processing and distribution system. The EOS Data and Information System (EOSDIS) is the archiving, production, and distribution system for data collected by the EOS space segment and uses a multilayer architecture for processing, archiving, and distributing EOS data. The first layer consists of the spacecraft ground stations and processing facilities that receive the raw data from the orbiting platforms and then separate the data by individual sensors. The second layer consists of Distributed Active Archive Centers (DAAC) that process, distribute, and archive the sensor data. The third layer consists of a user science processing network. The EOSDIS is being developed in a phased implementation. The initial phase, Version 0, is a prototype of the operational system. Version 0 activities are based upon existing systems and are designed to provide an EOSDIS-like capability for information management and distribution. An important science support task is the creation of simulated data sets for EOS instruments from precursor aircraft or satellite data. The Land Processes DAAC, at the EROS Data Center (EDC), is responsible for archiving and processing EOS precursor data from airborne instruments such as the Thermal Infrared Multispectral Scanner (TIMS), the Thematic Mapper Simulator (TMS), and Airborne Visible and Infrared Imaging Spectrometer (AVIRIS). AVIRIS, TIMS, and TMS are flown by the NASA-Ames Research Center ARC) on an ER-2. The ER-2 flies at 65000 feet and can carry up to three sensors simultaneously. Most jointly collected data sets are somewhat boresighted and roughly registered. The instrument data are being used to construct data sets that simulate the spectral and spatial characteristics of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument scheduled to be flown on the first EOS-AM spacecraft. The ASTER is designed to acquire 14 channels of land science data in the visible and near-IR (VNIR), shortwave-IR (SWIR), and thermal-IR (TIR) regions from 0.52 micron to 11.65 micron at high spatial resolutions of 15 m to 90 m. Stereo data will also be acquired in the VNIR region in a single band. The AVIRIS and TMS cover the ASTER VNIR and SWIR bands, and the TIMS covers the TIR bands. Simulated ASTER data sets have been generated over Death Valley, California, Cuprite, Nevada, and the Drum Mountains, Utah using a combination of AVIRIS, TIMS, amd TMS data, and existing digital elevation models (DEM) for the topographic information.

Mah, G. R.↗

Studies on Hot-Melt Prepregging of PMR-II-50 Polyimide Resin with Graphite Fibers

A Second generation PMR (in situ Polymerization of Monomer Reactants) polyimide resin, PMR-II-50, has been considered for high temperature and high stiffness space propulsion composites applications for its improved high temperature performance. As part of composite processing optimization, two commercial prepregging methods: solution vs. hot-melt processes were investigated with M40J fabrics from Toray. In a previous study a systematic chemical, physical, thermal and mechanical characterization of these composites indicated that poor resin-fiber interfacial wetting, especially for the hot-melt process, resulted in poor composite quality. In order to improve the interfacial wetting, optimization of the resin viscosity and process variables were attempted in a commercial hot-melt prepregging line. In addition to presenting the results from the prepreg quality optimization trials, the combined effects of the prepregging method and two different composite cure methods, i.e., hot press vs. autoclave on composite quality and properties are discussed.

Shin, E. Eugene↗

Studies on Hot-Melt Prepregging on PRM-II-50 Polyimide Resin with Graphite Fibers

A second generation PMR (in situ Polymerization of Monomer Reactants) polyimide resin PMR-II-50, has been considered for high temperature and high stiffness space propulsion composites applications for its improved high temperature performance. As part of composite processing optimization, two commercial prepregging methods: solution vs. hot-melt processes were investigated with M40J fabrics from Toray. In a previous study a systematic chemical, physical, thermal and mechanical characterization of these composites indicated the poor resin-fiber interfacial wetting, especially for the hot-melt process, resulted in poor composite quality. In order to improve the interfacial wetting, optimization of the resin viscosity and process variables were attempted in a commercial hot-melt prepregging line. In addition to presenting the results from the prepreg quality optimization trials, the combined effects of the prepregging method and two different composite cure methods, i.e. hot press vs. autoclave on composite quality and properties are discussed.

Shin, E. Eugene↗

Irrigation Characterization Improved by the Direct Use of SMAP Soil Moisture Anomalies Within a Data Assimilation System

Prior soil moisture data assimilation (DA) efforts to incorporate human management features such as agricultural irrigation has only shown limited success. This is partly due to the fact that observational rescaling approaches for bias correction used in soil moisture DA systems are less effective when unmodeled processes such as irrigation are the dominant source of systematic biases. In this article, we demonstrate an alternative approach, i.e. anomaly correction for overcoming this limitation. Unlike the rescaling approaches, the proposed method does not scale remote sensing soil moisture retrievals to the model climatology, but it extracts the temporal variability information from the retrievals. The study demonstrates this approach through the assimilation of soil moisture retrievals from the Soil Moisture Active Passive mission into the Noah land surface model. The results demonstrate that DA using the anomaly correction method can better capture the effect of irrigation on soil moisture in agricultural areas while providing comparable performance to the DA integrations using rescaling approaches in non-irrigated areas. These findings emphasize the need to reduce inconsistencies between remote sensing and the models so that assimilation methods can employ information from remote sensing more directly to develop representations of unmodeled processes such as irrigation.

soil moisture↗

The Development and Deployment of Machine Learning Models for Aircraft Engine Concept Assessment

In today's competitive landscape, the effective development and utilization of machine-learning (ML) applications have become imperative across various sectors. This study presents an outline of the procedure involved in creating and implementing ML models for conceptualizing and evaluating aircraft engines. These models leverage supervised deep-learning algorithms to analyze patterns within an open-source repository containing data on both production and research conventional turbofan engines. The main areas of focus encompass crucial engine parameters like thrust-specific fuel consumption (TSFC), engine weight, engine diameter, and turbomachinery stage counts. While the creation of ML models is fundamental for their utilization, ensuring their seamless deployment holds equal significance. To address this aspect, a conversational AI chatbot is constructed, utilizing natural language processing (NLP) techniques, to facilitate the deployment of these ML models. The comprehensive workflow encompasses several key stages: gathering and enhancing engine data, training and cross validating the ML models, testing and evaluating their performance, and finally, deploying, monitoring, and updating the ML models. By following this systematic approach, the aim is to streamline the development and deployment process of ML models tailored for aircraft engine assessment.

Development↗

The Development and Deployment of Machine Learning Models for Aircraft Engine Concept Assessment

In today's competitive landscape, the effective development and utilization of machine-learning (ML) applications have become imperative across various sectors. This study presents an outline of the procedure involved in creating and implementing ML models for conceptualizing and evaluating aircraft engines. These models leverage supervised deep-learning algorithms to analyze patterns within an open-source repository containing data on both production and research conventional turbofan engines. The main areas of focus encompass crucial engine parameters like thrust-specific fuel consumption (TSFC), engine weight, engine diameter, and turbomachinery stage counts. While the creation of ML models is fundamental for their utilization, ensuring their seamless deployment holds equal significance. To address this aspect, a conversational AI chatbot is constructed, utilizing natural language processing (NLP) techniques, to facilitate the deployment of these ML models. The comprehensive workflow encompasses several key stages: gathering and enhancing engine data, training and cross validating the ML models, testing and evaluating their performance, and finally, deploying, monitoring, and updating the ML models. By following this systematic approach, the aim is to streamline the development and deployment process of ML models tailored for aircraft engine assessment.

Development↗

Machine Learning Application in Aircraft Engine Conceptual Design

In the current competitive environment, the successful creation and application of machine learning (ML) technologies have become crucial across multiple industries. This study outlines the process of creating and implementing ML models for conceptualizing and evaluating aircraft engines. These models use supervised deep-learning algorithms to analyze patterns within an open-source repository containing data on both production and research conventional turbofan engines. Key focus areas include crucial engine parameters such as thrust-specific fuel consumption (TSFC), engine weight, engine diameter, and turbomachinery stage counts. While developing ML models is fundamental, ensuring their seamless deployment is equally important. To address this, a conversational AI chatbot is constructed using natural language processing (NLP) techniques to facilitate the deployment of these ML models. The comprehensive workflow includes several key stages: gathering and enhancing engine data, training and cross validating the ML models, testing and evaluating their performance, and finally, deploying, monitoring, and updating the ML models. By following this systematic approach, the aim is to streamline the development and deployment process of ML models tailored for aircraft engine conceptual design.

Aircraft Engine↗

A compositional classification scheme for meteoritic chondrules

A compositionally based classification scheme for chondrules is proposed that will help in systematizing the wealth of data available and disentangling the effects of nebular and subsequent processes. The classification is not by texture or the composition of a single phase, or a mixture of these two, but rather is a comprehensive, systematic approach which uses the composition of the two main chondrule components. This scheme is applicable to over 95 percent of the chondrules and is easily applied using an electron microprobe. It stresses the original diversity of the chondrules and the complex yet facile way in which they respond to parent-body metamorphism. Results using this classification scheme suggest that arguments against an important role of chondrules in determining the compositional trends of the chondrites have been premature.

Sears, Derek W. G.↗

The Development and Deployment of Machine Learning Models for Aircraft Engine Concept Assessment

In today's competitive landscape, the effective development and utilization of machine-learning (ML) applications have become crucial across diverse economic sectors. This study presents an outline of the procedure involved in creating and implementing ML models for conceptualizing and evaluating aircraft engines. These models leverage supervised deep-learning algorithms to analyze patterns within an open-source repository containing data on both production and research conventional turbofan engines. The main areas of focus encompass crucial engine parameters like thrust-specific fuel consumption (TSFC), engine weight, engine diameter, and turbomachinery stage counts. While the creation of ML models is fundamental for their utilization, ensuring their seamless deployment holds equal significance. To address this aspect, a conversational AI chatbot that specifically focuses on propulsion has been developed. Leveraging natural language processing (NLP) techniques, this chatbot simplifies the deployment of machine learning (ML) models. The comprehensive workflow encompasses several key stages: gathering and enhancing engine data, training and cross validating the ML models, testing and evaluating their performance, and finally, deploying, monitoring, and updating the ML models. By following this systematic approach, the aim is to streamline the development and deployment process of ML models tailored for aircraft engine assessment.

AI Chatbot↗

Tabular data base construction and analysis from thematic classified Landsat imagery of Portland, Oregon

A systematic verification of Landsat data classifications of the Portland, Oregon metropolitan area has been undertaken on the basis of census tract data. The degree of systematic misclassification due to the Bayesian classifier used to process the Landsat data was noted for the various suburban, industrialized and central business districts of the metropolitan area. The Landsat determinations of residential land use were employed to estimate the number of automobile trips generated in the region and to model air pollution hazards.

Bryant, N. A.↗

Sputtering - Its relationship to isotopic fractionation on the lunar surface

Silicon, oxygen, sulfur and, possibly, potassium show systematic enrichment of heavier isotopes with increasing soil maturity, probably as a result of solar-wind sputtering, but such a pattern is not exhibited by solar wind-implanted species. Apparently sputter-erosion is not penetrating to their implantation depth, about 200 A. This suggests that sputtering on the moon is being impeded by deposition of vapor condensate following meteoritic impact. Although neither secular variation in the isotopic composition at the source of regolith carbon, probably the solar wind, nor isotopic fractionation of carbon after implantation in the regolith can be ruled out, carbon isotope systematics currently yield no evidence in support of either process.

Kerridge, J. F.↗

Gravity field of Venus - A preliminary analysis

The gravitational field of Venus obtained by tracking the Pioneer Venus Orbiter is examined. For each spacecraft orbit, two hours of Doppler data centered around periapsis were used to estimate spacecraft position and velocity and the velocity residuals obtained were spline fit and differentiated to produce line of sight gravitational accelerations. Consistent variations in line of sight accelerations from orbit to orbit reveal the presence of gravitational anomalies. A simulation of isostatic compensation for an elevated region on the surface of Venus indicates that the mean depth of compensation is no greater than about 100 km. Gravitational spectra obtained from a Fourier analysis of line of sight accelerations from selected Venus orbits are compared to the earth's gravitational spectrum and spherical harmonic gravitational potential power spectra of the earth, the moon and Mars. The Venus power spectrum is found to be remarkably similar to that of the earth, however systematic variations in the harmonics suggest differences in dynamic processes or lithospheric behavior.

Phillips, R. J.↗

Preliminary spectral and geologic analysis of Landsat-4 Thematic Mapper data, Wind River Basin area, Wyoming

A Landsat-4 Thematic Mapper (TM) image of the Wind River Basin area in Wyoming is currently under analysis for stratigraphic and structural mapping and for assessment of spectral and spatial characteristics using visible, near infrared, and short wavelength infrared bands. To estimate the equivalent Lambertian surface reflectance, TM radiance data were calibrated to remove atmospheric and instrumental effects. Reflectance measurements for homogeneous natural and cultural targets were acquired about one year after data acquisition. Calibration data obtained during the analysis were used to calculate new gains and offsets to improve scanner response for earth science applications. It is shown that the principal component images calculated from the TM data were the result of linear transformations of ground reflectance. In images prepared from this transform, the separation of spectral classes was independent of systematic atmospheric and instrumental factors. Several examples of the processed images are provided.

Conel, J. E.↗

The role of NDE in ceramic turbine engine component development

A systematic approach to the use of nondestructive evaluation (NDE) throughout the ceramic engine component development cycle is necessary to achieve the high reliability required for turbine engine applications. Specifically, NDE techniques must be developed and applied to component design, development, fabrication, and test to ensure adequate materials characterization. This approach has the potential of reducing ceramic component cost by optimizing the initial design and eliminating unacceptable components early in the fabrication cycle. Although research NDE techniques have been developed that have the potential to solve the problems of ceramic component inspection, systematic application of these NDE techniques for inspection and process control has not yet occurred. The application of NDE techniques for both material characterization and flaw detection is discussed as well as an approach for applying NDE throughout the fabrication process to achieve highly reliable ceramic components.

Minter, Janet↗

Radio-planetary from tie from Phobos-2 VLBI data

In an ongoing effort to improve the knowledge of the relative orientation (the 'frame tie') of the planetary ephemeris reference frame used in deep navigation and a second reference frame that is defined by the coordinates of a set of extragalactic radio sources, VLBI observations of the Soviet Phobos-2 spacecraft and nearby (in angle) radio sources were obtained at two epochs in 1989, shortly after the spacecraft entered orbit about Mars. The frame tie is an important systematic error source affecting both interplanetary navigation and the process of improving the theory of the Earth's orientation. The data from a single Phobos-2 VLBI session measure one component of the direction vector from Earth to Mars in the frame of the extragalactic radio sources (the 'radio frame'). The radio frame has been shown to be stable and internally consistent with an accuracy of 5 nrad. The planetary ephemeris reference frame has an internal consistency of approximately 15 nrad. The planetary and radio source reference frames were aligned prior to 1989 and measurements of occulations of the radio source 3C273 by the Moon. The Phobos-2 VLBI measurements provide improvement in the accuracy of two of the three angles describing a general rotation between the planetary and radio reference frames. A complete set of measurements is not available because data acquisition was terminated prematurely by loss of spacecraft. The analysis of the two Phobos-2 VLBI data sets indicates that, in the directions of the two rotation components determined by these data, the JPL planetary ephemeris DE200 is aligned with the radio frame as adopted by the International Earth Rotation Service within an accuracy of 20-40 nrad, depending on direction. The limiting errors in the solutions for these offsets are spacecraft trajectory (20 nrad), instrumental biases (19 nrad), and dependence of quasar coordinates on observing frequency (24 nrad).

Hildebrand, C. E.↗

IRAS F15307+3252: A hyperluminous infrared galaxy at z = 0.93

A new hyperluminous infrared galaxy in the Infrared Astronomy Satellite (IRAS) Faint Source Catalog is identified at a redshift of 0.93. This object has a bolometric luminosity of approximately 10(exp 13) solar luminosity, a very large ratio of infrared-to-optical luminosity, warm dust emission, a ratio of infrared-to-radio flux densities consistent with other infrared galaxies, and an optical spectrum similar to a Seyfert 2 galaxy. IRAS F15307+3552 shares these characteristics and its radio-to-optical spectral energy distribution with two other infrared galaxies, F10214+4724 and P09104+4109. Discovery of a third object with these properties defines an extreme subclass of ultraluminous galaxies powered primarily by star formation. The systematic method used to find this object begins the process of determining the space density of these most luminous examples of the infrared galaxy phenomenon.

Cutri, Roc M.↗