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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
NASA NTRS1986

Imaging radar polarimetry from wave synthesis

It was shown that it is possible to measure the complete scattering matrix of an object using data acquired on a single aircraft pass, and can combine the signals later in the data processor to generate radar images corresponding to any desired combination of transmit and receive polarization. Various scattering models predict different dependence on polarization state of received power from an object. The imaging polarimeter permits determination of this dependence, which is called the polarization signature, of each point in a radar image. Comparison of the theoretical predictions and observational data yield identification of possible scattering mechanisms for each area of interest. It was found that backscatter from the ocean is highly polarized and well-modeled by Bragg scattering, while scattering from trees in a city park possesses a considerable unpolarized component. Urban regions exhibit the characteristics expected from dihedral corner reflectors and their polarization signatures are quite different from the one-bounce Bragg model.

Zebker, Howard A.↗
NASA NTRS1975

Interactive Image Processing for Meteorological Applications at NASA/Goddard Space Flight Center

The development of interactive image processing techniques which have led to meteorological data processing systems are discussed. Hardware and software functions of the existing Image Display and Manipulation System (IDAMS) and the special-purpose METPAK software package which has been developed for performing meteorology operations on IDAMS are described. A second-generation interactive image processing system, the Atmospheric and Oceanographic Information Processing System (AOIPS), which is under development, is also outlined. These systems are being designed to provide the necessary high throughput required for analysis of geostationary satellite data in the time domain.

Billingsley, J. B.↗
DOE OSTI2024

AGR-2 TRISO Shadow Imaging Archive

As a part of fuel quality control characterization, backlit shadow images of particles produced by the Advanced Gas Reactor Fuel Development and Qualification (AGR) Program’s AGR-2 irradiation campaign were acquired at Oak Ridge National Laboratory (ORNL). These images may be of use for the development of image processing algorithms with the benchmark values measured at ORNL. This report provides those benchmark values, along with the raw images and data generated by the ORNL shadow imaging process.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1993

A model-based approach for detection of objects in low resolution passive millimeter wave images

A model-based vision system to assist the pilots in landing maneuvers under restricted visibility conditions is described. The system was designed to analyze image sequences obtained from a Passive Millimeter Wave (PMMW) imaging system mounted on the aircraft to delineate runways/taxiways, buildings, and other objects on or near runways. PMMW sensors have good response in a foggy atmosphere, but their spatial resolution is very low. However, additional data such as airport model and approximate position and orientation of aircraft are available. These data are exploited to guide our model-based system to locate objects in the low resolution image and generate warning signals to alert the pilots. Also analytical expressions were derived from the accuracy of the camera position estimate obtained by detecting the position of known objects in the image.

Kasturi, Rangachar↗
NASA NTRS1993

A model-based approach for detection of objects in low resolution passive-millimeter wave images

We describe a model-based vision system to assist pilots in landing maneuvers under restricted visibility conditions. The system was designed to analyze image sequences obtained from a Passive Millimeter Wave (PMMW) imaging system mounted on the aircraft to delineate runways/taxiways, buildings, and other objects on or near runways. PMMW sensors have good response in a foggy atmosphere; but, their spatial resolution is very low. However, additional data such as airport model and approximate position and orientation of aircraft are available. We exploit these data to guide our model-based system to locate objects in the low resolution image and generate warning signals to alert the pilots. We also derive analytical expressions for the accuracy of the camera position estimate obtained by detecting the position of known objects in the image.

Tang, Yuan-Liang↗
NASA NTRS2002

The North Alabama Severe Thunderstorm Observations, Research, and Monitoring Network (STORMnet)

The Severe Thunderstorm Observations, Research, and Monitoring network (STORMnet) became operational in 2001 as a test bed to infuse new science and technologies into the severe and hazardous weather forecasting and warning process. STORMnet is collaboration among NASA scientists, National Weather Service (NWS) forecasters, emergency managers and other partners. STORMnet integrates total lightning observations from a ten-station 3-D VHF regional lightning mapping array, the National Lightning Detection Network (NLDN), real-time regional NEXRAD Doppler radar, satellite visible and infrared imagers, and a mobile atmospheric profiling system to characterize storms and their evolution. The storm characteristics and life-cycle trending are accomplished in real-time through the second generation Lightning Imaging Sensor Demonstration and Display (LISDAD II), a distributed processing system with a JAVA-based display application that allows anyone, anywhere to track individual storm histories within the Tennessee Valley region of north Alabama and Tennessee, a region of the southeastern U.S. well known for abundant severe weather.

Goodman, S. J.↗
NASA NTRS1995

Real-Time, Holographic, Dynamic Image-Storage Device

Solid-state device developed for high-speed acquisition, dynamic storage, and amplification of three-dimensional holographic images. Holograms generated via four-wave mixing in two or more photorefractive crystals (or subelements of single crystal) to create single-crystal or multicrystal oscillator. Apparatus provides dynamic storage of holographic image of object after electronic shutter closed to turn off object beam. Provides capability to store, amplify, process, and transmit time-varying, two-dimensional, spatial information. Developments include sensors, actuators, and optical computers operating at speeds on order of speed of light. Potential in applications in which need for high-speed acquisition and storage of three-dimensional holographic images.

Montgomery, Raymond C.↗
NASA NTRS1995

The VOrtex Ring Transit EXperiment (VORTEX) GAS project

Get Away Special (GAS) payload G-093, also called VORTEX (VOrtex Ring Transit EXperiment), is an investigation of the propagation of a vortex ring through a liquid-gas interface in microgravity. This process results in the formation of one or more liquid droplets similar to earth based liquid atomization systems. In the absence of gravity, surface tension effects dominate the drop formation process. The Shuttle's microgravity environment allows the study of the same fluid atomization processes as using a larger drop size than is possible on Earth. This enables detailed experimental studies of the complex flow processes encountered in liquid atomization systems. With VORTEX, deformations in both the vortex ring and the fluid surface will be measured closely for the first time in a parameters range that accurately resembles liquid atomization. The experimental apparatus will record images of the interactions for analysis after the payload has been returned to earth. The current design of the VORTEX payload consists of a fluid test cell with a vortex ring generator, digital imaging system, laser illumination system, computer based controller, batteries for payload power, and an array of housekeeping and payload monitoring sensors. It is a self-contained experiment and will be flown on board the Space Shuttle in a 5 cubic feet GAS canister. The VORTEX Project is entirely run by students at the University of Michigan but is overseen by a faculty advisor acting as the payload customer and the contact person with NASA. This paper summarizes both the technical and programmatic aspects of the VORTEX Project.

Bilen, Sven G.↗
DOE OSTI2025

Artificial intelligence-based predictive modeling for imaging neutral particle analyzers on the DIII-D tokamak

The Imaging Neutral Particle Analyzer (INPA) at DIII-D is a diagnostic system used to accurately resolve the energy and spatial distributions of fast ions in fusion plasmas. A novel artificial intelligence (AI) technique named INPA-net is based on Reservoir Computing Networks and developed here to predict active and passive signals produced by charge-exchange reactions from injected and edge-cold neutrals, respectively, in magnetically confined fusion plasmas. This model is trained using a set of 21 time domain signals between 0 s to 3.35 s that includes injected beam and thermal plasma information, and 6444 real 2D experimental images of the INPA in 12 plasma discharges at DIII-D. The trained neural network is able to forecast experimental images in real-time. The model achieves an R-squared value of 0.91, which is higher than the 0.83 value achieved by a simple linear regression model. This improvement highlights the model's enhanced predictive accuracy for measured images from the validation set. This AI approach is valuable due to its rapid response times and potential for integration into real-time plasma control systems. A version of this model capable of generating syntehic images would be useful for the real-time monitoring of fast-ion transport. A comprehensive sensitivity study reveals that INPA-net maintains high performance even with variations in the input parameters, indicating the model's robustness and reliability. While developed for the INPA, the underlying architecture is adaptable and may be applied to various 2D imaging diagnostics in fusion research.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1997

Emulating the Visual Receptive Field Properties of MST Neurons with a Template Model of Heading Estimation

We have previously proposed a computational neural-network model by which the complex patterns of retinal image motion generated during locomotion (optic flow) can be processed by specialized detectors acting as templates for specific instances of self-motion. The detectors in this template model respond to global optic flow by sampling image motion over a large portion of the visual field through networks of local motion sensors with properties similar to neurons found in the middle temporal (MT) area of primate extrastriate visual cortex. The model detectors were designed to extract self-translation (heading), self-rotation, as well as the scene layout (relative distances) ahead of a moving observer, and are arranged in cortical-like heading maps to perform this function. Heading estimation from optic flow has been postulated by some to be implemented within the medial superior temporal (MST) area. Others have questioned whether MST neurons can fulfill this role because some of their receptive-field properties appear inconsistent with a role in heading estimation. To resolve this issue, we systematically compared MST single-unit responses with the outputs of model detectors under matched stimulus conditions. We found that the basic physiological properties of MST neurons can be explained by the template model. We conclude that MST neurons are well suited to support heading estimation and that the template model provides an explicit set of testable hypotheses which can guide future exploration of MST and adjacent areas within the primate superior temporal sulcus.

Perrone, John A.↗
NASA NTRS1998

Emulating the visual receptive-field properties of MST neurons with a template model of heading estimation

We have proposed previously a computational neural-network model by which the complex patterns of retinal image motion generated during locomotion (optic flow) can be processed by specialized detectors acting as templates for specific instances of self-motion. The detectors in this template model respond to global optic flow by sampling image motion over a large portion of the visual field through networks of local motion sensors with properties similar to those of neurons found in the middle temporal (MT) area of primate extrastriate visual cortex. These detectors, arranged within cortical-like maps, were designed to extract self-translation (heading) and self-rotation, as well as the scene layout (relative distances) ahead of a moving observer. We then postulated that heading from optic flow is directly encoded by individual neurons acting as heading detectors within the medial superior temporal (MST) area. Others have questioned whether individual MST neurons can perform this function because some of their receptive-field properties seem inconsistent with this role. To resolve this issue, we systematically compared MST responses with those of detectors from two different configurations of the model under matched stimulus conditions. We found that the characteristic physiological properties of MST neurons can be explained by the template model. We conclude that MST neurons are well suited to support self-motion estimation via a direct encoding of heading and that the template model provides an explicit set of testable hypotheses that can guide future exploration of MST and adjacent areas within the superior temporal sulcus.

NASA Discipline Neuroscience↗
NASA NTRS1986

Increased sensitivity of a vidicon optical multichannel analyzer with a detachable electrostatic image intensifier

A substantial gain in sensitivity is reported through the addition of a commercially available electrostatic image intensifier to a silicon-intensified target vidicon system. This comparatively low cost modification allows cooled or room temperature operation, CW or pulsed detection, and ease of removal or attachment. A 40-mm, first-generation technology image intensifier was used in a configuration in which the photocathode end was at negative high voltage and the phosphor end was at ground. Cooling the vidicon detector did not result in improved SNR; more generally, a 50-fold sensitivity increase was observed for the image intensifier/vidicon system.

Snow, J. B.↗
DOE OSTI2023

Leveraging generative adversarial networks to create realistic scanning transmission electron microscopy images

Abstract The rise of automation and machine learning (ML) in electron microscopy has the potential to revolutionize materials research through autonomous data collection and processing. A significant challenge lies in developing ML models that rapidly generalize to large data sets under varying experimental conditions. We address this by employing a cycle generative adversarial network (CycleGAN) with a reciprocal space discriminator, which augments simulated data with realistic spatial frequency information. This allows the CycleGAN to generate images nearly indistinguishable from real data and provide labels for ML applications. We showcase our approach by training a fully convolutional network (FCN) to identify single atom defects in a 4.5 million atom data set, collected using automated acquisition in an aberration-corrected scanning transmission electron microscope (STEM). Our method produces adaptable FCNs that can adjust to dynamically changing experimental variables with minimal intervention, marking a crucial step towards fully autonomous harnessing of microscopy big data.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Annular Metallic Nuclear Fuel Informatics at 50 nm Resolution

U-10wt.% Zr (U-10Zr) based metallic fuel is the leading candidate for next-generation sodium cooled fast reactor in United States. Advanced post-irradiation characterization (from sub-nanometer to micrometer) helps to understand fuel microstructure and property change during irradiation, benefiting fuel qualification for commercial application. With high velocity image data generating method, an automatic way to extract the microstructural information quantitively can better serve the needs from post irradiation characterization. A trained machine learning model, named Decision Tree, is employed to categorize pores caused by fission gas release and to aid phase identification. This work presents a showcase of this approach on different irradiated U-10Zr metallic fuels. This quantitative data offers insights into the fission product migration and potentially thermal conductivity degradation. This information from machine learning will be fed into fuel design code for better prediction of fuel performance.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2013

Remote Observations of Reentering Spacecraft Including the Space Shuttle Orbiter

Flight measurement is a critical phase in development, validation and certification processes of technologies destined for future civilian and military operational capabilities. This paper focuses on several recent NASA-sponsored remote observations that have provided unique engineering and scientific insights of reentry vehicle flight phenomenology and performance that could not necessarily be obtained with more traditional instrumentation methods such as onboard discrete surface sensors. The missions highlighted include multiple spatially-resolved infrared observations of the NASA Space Shuttle Orbiter during hypersonic reentry from 2009 to 2011, and emission spectroscopy of comparatively small-sized sample return capsules returning from exploration missions. Emphasis has been placed upon identifying the challenges associated with these remote sensing missions with focus on end-to-end aspects that include the initial science objective, selection of the appropriate imaging platform and instrumentation suite, target flight path analysis and acquisition strategy, pre-mission simulations to optimize sensor configuration, logistics and communications during the actual observation. Explored are collaborative opportunities and technology investments required to develop a next-generation quantitative imaging system (i.e., an intelligent sensor and platform) with greater capability, which could more affordably support cross cutting civilian and military flight test needs.

Horvath, Thomas J.↗
NASA NTRS1989

Stereo image visualization of numerically simulated turbulence

Stereo imaging techniques are used in the current study to analyze the results of a direct Navier-Stokes simulation of a turbulent boundary layer. Coherent structures embedded within the unsteady, highly three-dimensional turbulent flow fields are significantly enhanced through the use of stereo computer graphic renderings. In the paper, the geometrical foundations of two different methods for generating stereo images are reviewed. Sufficient detail is provided to enable readers to code stereo display algorithms from scratch. The effects of varying the geometric parameters of the stereo algorithm are demonstrated. Sample image pairs from animations of the turbulence numerical simulation are presented in color. Stereo imagery, especially when used with time-evolving data, provides new insight into the physical processes responsible for the generation an maintenance of turbulence within boundary layers.

Robinson, Stephen K.↗
DOE OSTI2023

Crack detection in fuel cell electrodes using a spatial filtering technique for overcoming noisy backgrounds

Image processing is a powerful tool that allows for rapid and automated data parsing in settings that occupy large variable spaces and require large data sets. Feature detection on difficultly discerned backgrounds is a subset of image processing that facilitates the extraction of quantitative metrics from otherwise subjective data. Crack detection and quantification is an important capability in polymer electrolyte membrane fuel cell quality control, failure analysis, and optimization. This work presents a technique to perform crack detection and quantification which overcomes challenges faced by commonly used image segmentation techniques. We demonstrate the use of a geometrically filtered noise‐level detection technique to select a binary threshold value from which we then quantify how cracked a sample is. Furthermore, we demonstrate the accuracy of our technique using programmatically generated test images of known crack amounts and their performance on real‐world fuel cell catalyst layer samples.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2025

Quantitative Imaging of Cobalt Phthalocyanine Distribution on Carbon Nanotubes: A Deep Learning Approach to Catalyst Characterization

Electrochemical reduction of carbon dioxide (CO 2 ) offers a pathway to valuable products, with catalysts playing a crucial role. This study investigates the distribution of cobalt tetraaminophthalocyanine (CoPc-NH 2 ) immobilized on carbon nanotubes (CNTs), utilizing high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) to characterize CoPc-NH 2 distribution. A challenge in the quantitative HAADF-STEM analysis is the introduction of bias from manual Co atom identification. To address this, we developed and trained a convolutional neural network (CNN) using a data set generated from images of CoPc-NH 2 /CNT samples with varying Co loadings. The CNN, implemented in TensorFlow and Keras, facilitated Co atom detections. Analysis of the CNN-generated data confirmed a correlation between Co loading and surface density, consistent with findings from UV–vis spectroscopy. Furthermore, the application of Ripley’s L(d) function highlighted the presence of slight Co atom clustering. Furthermore, this work demonstrates the utility of the combined HAADF-STEM and CNN approach for providing spatially resolved information about catalyst distribution on nonplanar supports, revealing structural details that are typically lost through other characterization methods.

HAADF-STEM↗