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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 NTRS1990

A mixture neural net for multispectral imaging spectrometer processing

Each spatial region viewed by an imaging spectrometer contains various elements in a mixture. The elements present and the amount of each are to be determined. A neural net solution is considered. Initial optical neural net hardware is described. The first simulations on the component requirements of a neural net are considered. The pseudoinverse solution is shown to not suffice, i.e. a neural net solution is required.

Casasent, David↗
NASA NTRS1991

A neural net based architecture for the segmentation of mixed gray-level and binary pictures

A neural-net-based architecture is proposed to perform segmentation in real time for mixed gray-level and binary pictures. In this approach, the composite picture is divided into 16 x 16 pixel blocks, which are identified as character blocks or image blocks on the basis of a dichotomy measure computed by an adaptive 16 x 16 neural net. For compression purposes, each image block is further divided into 4 x 4 subblocks; a one-bit nonparametric quantizer is used to encode 16 x 16 character and 4 x 4 image blocks; and the binary map and quantizer levels are obtained through a neural net segmentor over each block. The efficiency of the neural segmentation in terms of computational speed, data compression, and quality of the compressed picture is demonstrated. The effect of weight quantization is also discussed. VLSI implementations of such adaptive neural nets in CMOS technology are described and simulated in real time for a maximum block size of 256 pixels.

Tabatabai, Ali↗
NASA NTRS1992

Estimating net solar radiation using Landsat Thematic Mapper and digital elevation data

A radiative transfer algorithm is combined with digital elevation and satellite reflectance data to model spatial variability in net solar radiation at fine spatial resolution. The method is applied to the tall-grass prairie of the 16 x 16 sq km FIFE site (First ISLSCP Field Experiment) of the International Satellite Land Surface Climatology Project. Spectral reflectances as measured by the Landsat Thematic Mapper (TM) are corrected for atmospheric and topographic effects using field measurements and accurate 30-m digital elevation data in a detailed model of atmosphere-surface interaction. The spectral reflectances are then integrated to produce estimates of surface albedo in the range 0.3-3.0 microns. This map of albedo is used in an atmospheric and topographic radiative transfer model to produce a map of net solar radiation. A map of apparent net solar radiation is also derived using only the TM reflectance data, uncorrected for topography, and the average field-measured downwelling solar irradiance. Comparison with field measurements at 10 sites on the prairie shows that the topographically derived radiation map accurately captures the spatial variability in net solar radiation, but the apparent map does not.

Dubayah, R.↗
NASA NTRS1985

Net Thermal Radiation in the Atmosphere of Venus

The four entry probes of the Pioneer Venus mission measured the radiative net flux in the atmosphere of Venus at latitudes of 60 deg N, 31 deg S, 27 deg S, and 4 deg N. The three higher latitude probes carried instruments (small probe net flux radiometers; SNFR) with external sensors. The measured SNFR net fluxes are too large below the clouds, but an error source and correction scheme have been found. The near-equatorial probe carried an infrared radiometer (LIR) which viewed the atmosphere through a window in the probe. The LIR measurements are reasonable in the clouds, but increase to physically unreasonable levels shortly below the clouds. The probable error source and a correction procedure are identified. Three main conclusions can be drawn from comparisons of the four corrected flux profiles with radiative transfer calculations: (1) thermal net fluxes for the sounder probe do not require a reduction in the Mode 3 number density as has been suggested, but the probe measurements as a whole are most consistent with a significantly reduced mode 3 contribution to the cloud opacity; (2) at all probe sites, the fluxes imply that the upper cloud contains a yet undetected source of IR opacity; and (3) beneath the clouds the fluxes at a given altitude increase with latitude, suggesting greater IR cooling below the clouds at high latitudes and water vapor mixing ratios of about 2-5 x 10(exp -5) near 60 deg, 2-5 x 10(exp -4) near 30 deg, and greater than 5 x 10(exp -4) near the equator. The suggested latitudinal variation of IR cooling is consistent with descending motions at high latitudes, and it is speculated that it could provide an important additional drive for the general circulation.

Sromovsky, L. A.↗
NASA NTRS1996

Discrete Deterministic and Stochastic Petri Nets

Petri nets augmented with timing specifications gained a wide acceptance in the area of performance and reliability evaluation of complex systems exhibiting concurrency, synchronization, and conflicts. The state space of time-extended Petri nets is mapped onto its basic underlying stochastic process, which can be shown to be Markovian under the assumption of exponentially distributed firing times. The integration of exponentially and non-exponentially distributed timing is still one of the major problems for the analysis and was first attacked for continuous time Petri nets at the cost of structural or analytical restrictions. We propose a discrete deterministic and stochastic Petri net (DDSPN) formalism with no imposed structural or analytical restrictions where transitions can fire either in zero time or according to arbitrary firing times that can be represented as the time to absorption in a finite absorbing discrete time Markov chain (DTMC). Exponentially distributed firing times are then approximated arbitrarily well by geometric distributions. Deterministic firing times are a special case of the geometric distribution. The underlying stochastic process of a DDSPN is then also a DTMC, from which the transient and stationary solution can be obtained by standard techniques. A comprehensive algorithm and some state space reduction techniques for the analysis of DDSPNs are presented comprising the automatic detection of conflicts and confusions, which removes a major obstacle for the analysis of discrete time models.

MATHEMATICAL MODELS↗
NASA NTRS1985

Net Thermal Radiation in the Atmosphere of Venus

The four entry probes of the Pioneer Venus mission measured the radiative net flux in the atmosphere of Venus at latitudes of 60 deg. N, 31 deg. S, 27 deg. S, and 4 deg. N. The three higher latitude probes carried instruments (small probe net flux radiometers; SNFR) with external sensors. The measured SNFR net fluxes are too large below the clouds, but an error source and correction scheme have been found (H. E. Revercomb, L. A. Sromovsky, and V. E. Suomi, 1982, Icarus 52, 279-300). The near-equatorial probe carried an infrared radiometer (LIR) which viewed the atmosphere through a window in the probe. The LIR measurements are reasonable in the clouds, but increase to physically unreasonable levels shortly below the clouds. The probable error source and a correction procedure are identified. Three main conclusions can be drawn from comparisons of the four corrected flux profiles with radiative transfer calculations: (1) thermal net fluxes for the sounder probe do not require a reduction in the Mode 3 number density as has been suggested by O.B. Toon, B. Ragent, D. Colburn, J. Blamont, and C. Cot (1964. Icarus 37, 143-160), but the probe measurements as a whole are most consistent with a significantly reduced mode 3 contribution to the cloud opacity; (2) at all probe sites, the fluxes imply that the upper cloud contains a yet undetected source of IR opacity; and (3) beneath the clouds the fluxes at a given altitude increase with latitude, suggesting greater IR cooling below the clouds a( high latitudes and water vapor mixing ratios of about 2-5 x 10(exp -5) near 6 deg., 2-5 x 10(exp -11) near 30 deg., and less than 5 x 10(exp -4 ) near the equator. The suggested latitudinal variation of IR cooling is consistent with descending motions at high latitudes, and it is speculated that it could provide an important additional drive for the general circulation.

Revercomb, H. E.↗
NASA NTRS2000

Analysis of Phase-Type Stochastic Petri Nets With Discrete and Continuous Timing

The Petri net formalism is useful in studying many discrete-state, discrete-event systems exhibiting concurrency, synchronization, and other complex behavior. As a bipartite graph, the net can conveniently capture salient aspects of the system. As a mathematical tool, the net can specify an analyzable state space. Indeed, one can reason about certain qualitative properties (from state occupancies) and how they arise (the sequence of events leading there). By introducing deterministic or random delays, the model is forced to sojourn in states some amount of time, giving rise to an underlying stochastic process, one that can be specified in a compact way and capable of providing quantitative, probabilistic measures. We formalize a new non-Markovian extension to the Petri net that captures both discrete and continuous timing in the same model. The approach affords efficient, stationary analysis in most cases and efficient transient analysis under certain restrictions. Moreover, this new formalism has the added benefit in modeling fidelity stemming from the simultaneous capture of discrete- and continuous-time events (as opposed to capturing only one and approximating the other). We show how the underlying stochastic process, which is non-Markovian, can be resolved into simpler Markovian problems that enjoy efficient solutions. Solution algorithms are provided that can be easily programmed.

Jones, Robert L.↗
NASA NTRS2001

Neural-Net Processed Characteristic Patterns for Measurement of Structural Integrity of Pressure Cycled Components

A neural-net inspection process has been combined with a bootstrap training procedure and electronic holography to detect changes or damage in a pressure-cycled International Space Station cold plate to be used for cooling instrumentation. The cold plate was excited to vibrate in a normal mode at low amplitude, and the neural net was trained by example to flag small changes in the mode shape. The NDE (nondestructive-evaluation) technique is straightforward but in its infancy; its applications are ad-hoc and uncalibrated. Nevertheless previous research has shown that the neural net can detect displacement changes to better than 1/100 the maximum displacement amplitude. Development efforts that support the NDE technique are mentioned briefly, followed by descriptions of electronic holography and neural-net processing. The bootstrap training procedure and its application to detection of damage in a pressure-cycled cold plate are discussed. Suggestions for calibrating and quantifying the NDE procedure are presented.

Decker, A. J.↗
NASA NTRS1990

Catamaran Nets

West Coast Netting, Inc.'s net of Hyperester twine, is made of three strands of fiber twisted together by a company-invented sophisticated twisting machine and process that maintain precisely the same tension on each strand. The resulting twine offers higher strength and improved abrasion resistance. The technology that created the Hyperester supertwine has found spinoff applications, first as an extra-efficient seine for tuna fishing, then as a capture net for law enforcement agencies. The newest one is as a deck for racing catamarans. Hyperester twine net has been used on most of the high performance racing catamarans of recent years, including the America's Cup Challenge boats. They are tough and hold up well in the continual exposure to sunlight and saltwater.

Source record↗
NASA NTRS1961

Investigation of Net-Thrust and Base-Pressure Characteristics of Cylindrical Afterbodies with Clustered Supersonic Nozzles at Transonic Mach Numbers

A wind-tunnel investigation has been conducted at Mach numbers from 0.9 to 1.4 to determine the net-thrust and base-pressure characteristics of cylindrical afterbodies having clustered supersonic nozzles. The design Mach numbers of the nozzles were 2.0 and 2.5 and the number of clustered nozzles ranged from two to six. The nozzles had throat-to- base diameter ratios of 0.155, 0.225, 0.278, and 0.320. Some models were tested with various configurations of extended, shrouded, flush, and canted nozzles. The nozzles discharged unheated air from the base at ratios of jet total pressure to free-stream static pressure ranging from 1 to approximately 20. The results of this investigation showed that both the ratio of total exit area to base area and the number of jets affect the net-thrust factor to a significant degree for the extended-nozzle configurations. Good net-thrust factors were obtained with all the model configurations near the design jet total-pressure ratio; however, the extended-nozzle configuration had the highest net-thrust factor over the test jet total- pressure-ratio range. Canting the twin nozzles outward resulted in a favorable thrust factor over a limited jet total-pressure-ratio range, and surrounding the nozzles with a single shroud reduced thrust factors for the range of jet total-pressure ratio of this investigation.

Andrews, Earl H., Jr.↗
NASA NTRS2004

Examples of Current and Future Uses of Neural-Net Image Processing for Aerospace Applications

Feed forward artificial neural networks are very convenient for performing correlated interpolation of pairs of complex noisy data sets as well as detecting small changes in image data. Image-to-image, image-to-variable and image-to-index applications have been tested at Glenn. Early demonstration applications are summarized including image-directed alignment of optics, tomography, flow-visualization control of wind-tunnel operations and structural-model-trained neural networks. A practical application is reviewed that employs neural-net detection of structural damage from interference fringe patterns. Both sensor-based and optics-only calibration procedures are available for this technique. These accomplishments have generated the knowledge necessary to suggest some other applications for NASA and Government programs. A tomography application is discussed to support Glenn's Icing Research tomography effort. The self-regularizing capability of a neural net is shown to predict the expected performance of the tomography geometry and to augment fast data processing. Other potential applications involve the quantum technologies. It may be possible to use a neural net as an image-to-image controller of an optical tweezers being used for diagnostics of isolated nano structures. The image-to-image transformation properties also offer the potential for simulating quantum computing. Computer resources are detailed for implementing the black box calibration features of the neural nets.

Decker, Arthur J.↗
NASA NTRS2005

Net-Shape HIP Powder Metallurgy Components for Rocket Engines

True net shape consolidation of powder metal (PM) by hot isostatic pressing (HIP) provides opportunities for many cost, performance and life benefits over conventional fabrication processes for large rocket engine structures. Various forms of selectively net-shape PM have been around for thirty years or so. However, it is only recently that major applications have been pursued for rocket engine hardware fabricated in the United States. The method employs sacrificial metallic tooling (HIP capsule and shaped inserts), which is removed from the part after HIP consolidation of the powder, by selective acid dissolution. Full exploitation of net-shape PM requires innovative approaches in both component design and materials and processing details. The benefits include: uniform and homogeneous microstructure with no porosity, irrespective of component shape and size; elimination of welds and the associated quality and life limitations; removal of traditional producibility constraints on design freedom, such as forgeability and machinability, and scale-up to very large, monolithic parts, limited only by the size of existing HIP furnaces. Net-shape PM HIP also enables fabrication of complex configurations providing additional, unique functionalities. The progress made in these areas will be described. Then critical aspects of the technology that still require significant further development and maturation will be discussed from the perspective of an engine systems builder and end-user of the technology.

Bampton, Cliff↗
NASA NTRS2011

Evaluation of Advanced Stirling Convertor Net Heat Input Correlation Methods Using a Thermal Standard

Test hardware used to validate net heat prediction models. Problem: Net Heat Input cannot be measured directly during operation. Net heat input is a key parameter needed in prediction of efficiency for convertor performance. Efficiency = Electrical Power Output (Measured) divided by Net Heat Input (Calculated). Efficiency is used to compare convertor designs and trade technology advantages for mission planning.

Briggs, Maxwell↗
NASA NTRS2018

NeMO-Net & Fluid Lensing: The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment Using Fluid Lensing Augmentation of NASA EOS Data

We present preliminary results from NASA NeMO-Net, the first neural multi-modal observation and training network for global coral reef assessment. NeMO-Net is an open-source deep convolutional neural network (CNN) and interactive active learning training software in development which will assess the present and past dynamics of coral reef ecosystems. NeMO-Net exploits active learning and data fusion of mm-scale remotely sensed 3D images of coral reefs captured using fluid lensing with the NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as hyperspectral airborne remote sensing data from the ongoing NASA CORAL mission and lower-resolution satellite data to determine coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. Aquatic ecosystems, particularly coral reefs, remain quantitatively misrepresented by low-resolution remote sensing as a result of refractive distortion from ocean waves, optical attenuation, and remoteness. Machine learning classification of coral reefs using FluidCam mm-scale 3D data show that present satellite and airborne remote sensing techniques poorly characterize coral reef percent living cover, morphology type, and species breakdown at the mm, cm, and meter scales. Indeed, current global assessments of coral reef cover and morphology classification based on km-scale satellite data alone can suffer from segmentation errors greater than 40%, capable of change detection only on yearly temporal scales and decameter spatial scales, significantly hindering our understanding of patterns and processes in marine biodiversity at a time when these ecosystems are experiencing unprecedented anthropogenic pressures, ocean acidification, and sea surface temperature rise. NeMO-Net leverages our augmented machine learning algorithm that demonstrates data fusion of regional FluidCam (mm, cm-scale) airborne remote sensing with global low-resolution (m, km-scale) airborne and spaceborne imagery to reduce classification errors up to 80% over regional scales. Such technologies can substantially enhance our ability to assess coral reef ecosystems dynamics.

satellite data↗
NASA NTRS2019

Next-Generation Optical Sensing Technologies for Exploring Ocean Worlds - NASA FluidCam, MiDAR, and NeMO-Net

We highlight three emerging NASA optical technologies that enhance our ability to remotely sense, analyze, and explore ocean worlds–FluidCam and fluid lensing, MiDAR, and NeMO-Net. Fluid lensing is the first remote sensing technology capable of imaging through ocean waves without distortions in 3D at sub-cm resolutions. Fluid lensing and the purpose-built FluidCam CubeSat instruments have been used to provide refraction-corrected 3D multispectral imagery of shallow marine systems from unmanned aerial vehicles (UAVs). Results from repeat 2013 and 2016 airborne fluid lensing campaigns over coral reefs in American Samoa present a promising new tool for monitoring fine-scale ecological dynamics in shallow aquatic systems tens of square kilometers in area. MiDAR is a recently-patented active multispectral remote sensing and optical communications instrument which evolved from FluidCam. MiDAR is being tested on UAVs and autonomous underwater vehicles (AUVs) to remotely sense living and non-living structures in light-limited and analog planetary science environments. MiDAR illuminates targets with high-intensity narrowband structured optical radiation to measure an object’s spectral reflectance while simultaneously transmitting data. MiDAR is capable of remotely sensing reflectance at fine spatial and temporal scales, with a signal-to-noise ratio 10-10(exp 3) times higher than passive airborne and spaceborne remote sensing systems, enabling high-framerate multispectral sensing across the ultraviolet, visible, and near-infrared spectrum. Preliminary results from a 2018 mission to Guam show encouraging applications of MiDAR to imaging coral from airborne and underwater platforms whilst transmitting data across the air-water interface. Finally, we share NeMO-Net, the Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment. NeMO-Net is a machine learning technology under development that exploits high-resolution data from FluidCam and MiDAR for augmentation of low-resolution airborne and satellite remote sensing. NeMO-Net is intended to harmonize the growing diversity of 2D and 3D remote sensing with in situ data into a single open-source platform for assessing shallow marine ecosystems globally using active learning for citizen-science based training. Preliminary results from four-class Q17 coral classification have an accuracy of 94.4%. Together, these maturing technologies present promising scalable, practical, and cost-efficient innovations that address current observational and technological challenges in optical sensing of marine systems.

Ved Chirayath↗
NASA NTRSDate not supplied

Expanding NeMO-Net Machine Learning Capabilities for Citizen Science

NASA NeMO-Net, the neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive citizen science video game, released this April, for desktop and iOS devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using diver photomosaic imagery, the UAV enabled NASA FluidCam instrument, and satellite datasets. To date, the application has had over 40,000 downloads and over 60,000 unique coral reef classifications, each filtered through a user-based rating and expert evaluation system. We also present results from NeMO-Net’s convolutional neural network (CNN) models used to semantically segment 2D satellite imagery as well as projections of 3D coral reconstructions using user input data as training datasets. Fusing datasets using machine learning from multiple remote sensing platforms presents novel methodologies for assessing the health of coral ecosystems, which are critically endangered by a changing climate. In partnering with Mission Blue, the National Oceanic and Atmospheric Administration (NOAA), and the Living Oceans Foundation (LOF), NeMO-Net leverages an international consortium of subject matter experts to provide both proper training for citizen scientists and the generation of a labeled datasets to ingest into machine learning algorithms for global coral reef identification.

NeMO-Net↗
NASA NTRSDate not supplied

Expanding NeMO-Net Machine Learning Capabilities for Citizen Science

NASA NeMO-Net, the neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive citizen science video game, released this April, for desktop and iOS devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using diver photomosaic imagery, the UAV enabled NASA FluidCam instrument, and satellite datasets. To date, the application has had over 40,000 downloads and over60,000 unique coral reef classifications, each filtered through a user-based rating and expert evaluation system. We also present results from NeMO-Net’s convolutional neural network (CNN) models used to semantically segment 2D satellite imagery as well as projections of 3D coral reconstructions using user input data as training datasets. Fusing datasets using machine learning from multiple remote sensing platforms presents novel methodologies for assessing the health of coral ecosystems, which are critically endangered by a changing climate. In partnering with Mission Blue, the National Oceanic and Atmospheric Administration (NOAA), and the Living Oceans Foundation (LOF), NeMO-Net leverages an international consortium of subject matter experts to provide both proper training for citizen scientists and the generation of a labeled datasets to ingest into machine learning algorithms for global coral reef identification.

NeMO-Net↗