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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 NTRS2012

OpTIIX: An ISS-Based Testbed Paving the Roadmap Toward a Next Generation Large Aperture UV/Optical Space Telescope

The next generation large aperture UV/Optical space telescope will need a diameter substantially larger than even that of JWST in order to address some of the most compelling unanswered scientific quests. These quests include understanding the earliest phases of the Universe and detecting life on exo-planets by studying spectra of their atmospheres. Such 8-16 meter telescopes face severe challenges in terms of cost and complexity and are unlikely to be affordable unless a new paradigm is adopted for their design and construction. The conventional approach is to use monolithic or preassembled segmented mirrors requiring complicated and risky deployments and relying on future heavy-lift vehicles, large fairings and complex geometry. The new paradigm is to launch component modules on relatively small vehicles and then perform in-orbit robotic assembly of those modules. The Optical Testbed and Integration on ISS eXperiment (OpTIIX) is designed to demonstrate, at low cost by leveraging the infrastructure provided by ISS, telescope assembly technologies and end-to-end optical system technologies. The use of ISS as a testbed permits the concentration of resources on reducing the technical risks associated with robotically integrating the components. These include laser metrology and wavefront sensing and control (WFS&C) systems, an imaging instrument, lightweight, low-cost deformable primary mirror segments and the secondary mirror. These elements are then aligned to a diffraction-limited optical system in space. The capability to assemble the optical system and remove and replace components via the existing ISS robotic systems like the Special Purpose Dexterous Manipulator (SPDM), or by the ISS flight crew, allows for future experimentation, as well as repair.

Carpenter, Kenneth G.↗
NASA NTRS1978

Color enhancement of landsat agricultural imagery: JPL LACIE image processing support task

Color enhancement techniques were applied to LACIE LANDSAT segments to determine if such enhancement can assist analysis in crop identification. The procedure involved increasing the color range by removing correlation between components. First, a principal component transformation was performed, followed by contrast enhancement to equalize component variances, followed by an inverse transformation to restore familiar color relationships. Filtering was applied to lower order components to reduce color speckle in the enhanced products. Use of single acquisition and multiple acquisition statistics to control the enhancement were compared, and the effects of normalization investigated. Evaluation is left to LACIE personnel.

Madura, D. P.↗
NASA NTRS1988

Parallel algorithm for determining motion vectors in ice floe images by matching edge features

A parallel algorithm is described to determine motion vectors of ice floes using time sequences of images of the Arctic ocean obtained from the Synthetic Aperture Radar (SAR) instrument flown on-board the SEASAT spacecraft. Researchers describe a parallel algorithm which is implemented on the MPP for locating corresponding objects based on their translationally and rotationally invariant features. The algorithm first approximates the edges in the images by polygons or sets of connected straight-line segments. Each such edge structure is then reduced to a seed point. Associated with each seed point are the descriptions (lengths, orientations and sequence numbers) of the lines constituting the corresponding edge structure. A parallel matching algorithm is used to match packed arrays of such descriptions to identify corresponding seed points in the two images. The matching algorithm is designed such that fragmentation and merging of ice floes are taken into account by accepting partial matches. The technique has been demonstrated to work on synthetic test patterns and real image pairs from SEASAT in times ranging from .5 to 0.7 seconds for 128 x 128 images.

Manohar, M.↗
NASA NTRS2001

Use of Yohkoh SXT in Measuring the Net Current and CME Productivity of Active Regions

In our investigation of the correlation of global nonpotentiality of active regions to their CME productivity (Falconer, D.A. 2001, JGR, in press, and Falconer, Moore, & Gary, 2000, EOS 82, 20 S323), we use Yohkoh SXT images for two purposes. The first use is to help resolve the 180 degree ambiguity in the direction of the observed transverse magnetic field. Resolution of the 180 degree ambiguity is important, since the net current, one of our measures of global nonpotentiality, is derived from integrating the dot product of the transverse field around a contour (I(sub N)=(integral)BT(raised dot)dl). The ambiguity results from the observed transverse field being determined from the linear polarization, which gives the plane of the direction, but leaves a 180 degrees ambiguity. Automated methods to resolve the ambiguity ranging from the simple acute angle rule (Falconer, D.A. 2001) to the more sophisticated annealing method (Metcalf T.R. 1994). For many active regions, especially ones that are nearly potential these methods work well. But for very nonpotential active regions where the shear angle (the angle between the observed and potential transverse field) is near 90 degrees throughout large swaths along the main neutral line, both methods can resolve the ambiguity incorrectly for long segments of the neutral line. By determining from coronal images, such as those from Yohkoh/SXT, the sense of shear along the main neutral line in the active region, these cases can be identified and corrected by a modification of the acute angle rule described here. The second use of Yohkoh/SXT in this study is to check for the cusped coronal arcades of long-duration eruptive flares. This signature is an excellent proxy for CMEs, and was used by Canfield, Hudson, and McKenzie (1999 GRL V26, 6, 627-630). This work is funded by NSF through the Space Weather Program and by NASA through the Solar Physics Supporting Research and Technology Program.

Falconer, D. A.↗
NASA NTRSDate not supplied

The Porous Microstructure Analysis (PuMA) software

The open-source Porous Microstructure Analysis (PuMA) software was implemented to offer an efficient framework for determining material characteristics from 3D microstructures. Its development was inspired by progress in X-ray microtomography, an imaging technology that captures the internal structure of materials in 3D, and even in a 4D temporal context. Over recent years, this method has transformed the domain of materials science due to its capability to non-destructively examine material microstructures while presenting digital data about their geometrical details. It has provided insights into materials relevant to several NASA missions, including heatshields, parachute fabrics, meteorites, and other advanced composites. PuMA, in its current version 3, delivers an array of features, spanning from basic geometric insights of a microstructure to intricate anisotropic thermo-elastic and chemical behavior. Specifically, the software evaluates morphological attributes (specific surface area, volume fractions, mean intercept lengths, orientation) and physical characteristics (conductivity, elasticity, permeability, and tortuosity). Additionally, it can model material degradation processes, such as oxidation and surface chemistry interactions. The software can generate synthetic microstructures, from straightforward geometrical designs to intricate woven and non-woven geometries. Coupling material generation and characterization enable parametric studies and sensitivity analysis to optimize the microstructural performance and inform design decisions and reliability assessment based on uncertainty quantification. A recent addition to PuMA includes the TomoSAM plugin, devised to incorporate the cutting-edge Segment Anything Model (SAM). SAM is a promptable deep learning model that can identify objects and create image masks in a zero-shot manner, based only on a few user clicks. The synergy between these tools aids in the segmentation of complex 3D datasets from tomography or other imaging techniques, which would otherwise require a laborious manual segmentation process.

Tomography↗
NASA NTRS1993

Direct 75 milliarcsecond images from the Multiple Mirror Telescope with adaptive optics

We report results from an adaptive optics system designed to provide imaging at the diffraction limit of resolution in the near-infrared at the Multiple Mirror Telescope (MMT). For the present experiment, the aperture consisted of five of the six primary mirrors of the MMT, operating as a coherently phased array. The largest components of the atmospherically induced wave-front aberration are the fluctuations in mean phase between the segments. These errors were derived in real time from the Fourier transform of short-exposure stellar images at 2.2 microns and corrected at an image of the telescope pupil with piston motion from a segmented adaptive mirror. At a correction rate of 43 Hz, this level of adaptive control resulted in an integrated image with a clear diffraction-limited component of 0.075 arcsec FWHM. This stabilized component is present directly in the light arriving at the detector and is not the result of postprocessing. We discuss future improvements to our adaptive wave-front control and its application to astronomical observations.

Lloyd-Hart, M.↗
DOE OSTI2024

Segmentation and Classification of Fission as Pores in Reactor Irradiated Annular U–10Zr Metallic Fuel Using Machine Learning Models

Metallic fuels, particularly U—10Zr, are promising candidates for next-generation sodium-cooled fast reactors. Irradiation of nuclear fuels in reactors can lead to the formation of solid and gas fission product which subsequently forms microstructural pores, deteriorating fuel performance. Due to the massive amount of pores and complex phases formed, a quantitative description of fission gas pores is not yet available, preventing the development of microstructure-informed fuel performance modeling for fuel qualification. This paper applied a pre-trained deep learning model to ~10,260 high magnification scanning electron microscopy images. This method increased the accuracy of fission gas pore segmentation and allows statistical features to be extracted which cannot be achieved manually. A pre-trained decision tree model worked on the segemenation results and further classified the pores into different categories to produce a correlation between the pores, movement of lanthanides, and temperature gradient during irradiation. Finally, this paper emphasizes the potentials of machine learning models to accelerate fuel research, development, and qualification for advanced reactors.

36 MATERIALS SCIENCE↗
NASA NTRS2005

Measuring Low-Order Aberrations in a Segmented Telescope

The in-focus PSF optimizer (IPO) is an algorithm for use in monitoring and controlling the alignment of the segments of a segmented-mirror astronomical telescope. IPO is so named because it computes wave-front aberrations of the telescope from digitized pointspread functions (PSFs) measured in infocus images. Inasmuch as distant astronomical objects that behave optically as point sources can typically be seen in almost any astronomical image, the main benefit afforded by IPO may be to enable maintenance of mirror-segment alignments without detracting from valuable scientific-observation time. IPO evolved from prescription-retrieval type algorithms. Prescription retrieval uses in-focus and out-of-focus PSFs to infer the state of an imaging optical system. The state, in this context, refers to the positions, orientations, and low-order figure errors of the optical elements in the system. Both prescription- retrieval and IPO use an iterative, nonlinear, least-squares optimizer to compute the optimal state parameters such that a digital computer-generated model image matches the digitized image acquired from the real system. The difference between IPO and prescription- retrieval algorithms is that IPO is specifically designed to utilize infocus images only. Although the restriction to in-focus images limits IPO to calculating only the lowest-order wave front aberrations, it also causes the resulting computation to take much less time because fewer degrees of freedom are included in the optimization process. In the prescription retrieval software developed at JPL, the model images are generated using the ray-trace/physical optics program, MACOS. IPO, on the other hand, uses a linear sensitivity matrix to compute the exit-pupil wave front from the system parameters; the wave front is then converted into a complex pupil field, which is then propagated to the image plane via a fast Fourier transform. This approach is computationally faster and requires less computer memory than is needed for prescription retrieval.

Ohara, Catherine↗
NASA NTRS2018

Imaging for Hypersonic Experimental Aeroheating Testing (IHEAT) Version 4.0: User Manual

The IHEAT v4.0 software is a data reduction code for global thermography data acquired in the NASA Langley Aerothermodynamics Laboratory (LAL) hypersonic wind tunnels. IHEAT uses red and green color-intensity data from two-dimensional images of wind tunnel models to compute temperatures and heat-transfer rates using a semi-infinite, one-dimensional heat transfer approximation at each image pixel. Multiple automated tools in IHEAT v4.0 decrease the time required to reduce the data from a phosphor thermography wind tunnel run. Data at one or all of the image pixel locations can be exported to computer files for further analysis. The prior version of IHEAT, v3.2, was written in PV-WAVE® (now owned by Rogue Wave® Software) in 1994 and was limited in functionality to fit within the memory constraints of the available computers at the time. IHEAT v4.0 is written in MATLAB® by MathWorks® and contains several new features that leverage the increase in available memory of the current computers. A Piecewise tool permits the user to extract data along a segmented line cut that can follow interesting features in the image better than the single, straight line cuts that were possible with the legacy Length and Profile tools. The new Load Run and Batch tools facilitate batch processing by loading in all of the input files and images for a run at the same time. Load Run permits the user to process the available run images manually, while Batch automatically saves heat transfer data from all of the images based on the analysis previously performed on a single frame. IHEAT v4.0 also can automatically calculate the temporal collapse of reference line cuts from the time history heating data for a run to indicate the appropriate frame to reduce for each run. The IHEAT v4.0 source code was compiled into a standalone executable file that can be accessed remotely from several computers with different operating systems, simultaneously. The software is run through the MATLAB® Compiler Runtime engine, and therefore, IHEAT does not require a software license to run. Any software commands executed in the IHEAT v4.0 code will not affect other similar applications running on the same machine. Similarly, changes to the parent software do not affect a compiled code. These features of IHEAT v4.0 are improvements over the legacy v3.2 code, which required regular maintenance to avoid losing functionality as the PVWAVE ® programming language was upgraded.

Mason, Michelle L.↗
DOE OSTI2025

Bridging multimodal microscopy for advanced characterization on nuclear fuel using machine learning

Uranium dioxide (UO 2 ), widely used as driver fuel in light water reactors, experiences microstructure and property change by nuclear fission reactions. This paper bridges the characterization of fresh UO 2 fuel at different length scales, serving as a baseline for future post irradiation examination of irradiated UO 2 fuel. To characterize the microstructural change of nuclear fuel, modern approaches cover a wide range of length scales through different characterization techniques, such as mm scale for Synchrotron-based X-ray computed tomography (SXCT) and microscale for focused ion beam (FIB) and scanning electron microscopy (SEM). It is challenging to bridge the data and knowledge of the same sample in different length scales. This paper proposed a deep learning framework leveraging transfer learning to detect microstructural defects, trained from a sparse FIB, SEM, and SXCT images. The proposed model achieved superior performance in defect segmentation on multiscale microscopic data compared to four of the latest deep learning models.

36 MATERIALS SCIENCE↗
NASA NTRS1986

Computer vision for robots; Proceedings of the Meeting, Cannes, France, December 2-6, 1985

The conference presents papers on segmentation techniques, three-dimensional recognition and representation, processing image sequences, and navigation and mobility. Particular attention is given to determining the pose of an object, adaptive least squares correlation with geometrical constraints, and the reliable formation of feature vectors for two-dimensional shape representation. Other topics include the real-time tracking of a target moving on a natural textured background, computer vision for the guidance of roving robots, and integrating sensory data for object recognition tasks.

Faugeras, O. D.↗
NASA NTRS1988

An interactive testbed for development of expert tools for pattern recognition

The initial implementation of an interactive testbed for development of expert system applications in image processing, i.e., a toolbox of procedures designed to facilitate the capture of expert knowledge for region grouping and analysis is described. The user can elect to interactively enter commands (via a command interpreter) for region manipulation to, in effect, simulate the actions of a hypothetical expert system. The user can then incorporate any rules and procedures as derived from interactive experimentation into customized region processing procedures using the library of utility functions. An iterative technique based on image pyramids is used to compute the initial region segmentation without the use of process parameters. These regions can then be interactively examined and manipulated using the command interpreter.

Wharton, Stephen W.↗
NASA NTRS1991

Spatial Light Modulator Would Serve As Electronic Iris

In proposed technique for controlling brightness of image formed by lens, spatial light modulator serves as segmented, electronically variable aperture. Offers several advantages: spatial light modulator controlled remotely and responds faster than motorized iris or other remotely controlled mechanical iris. Unlike iris, modulator also configured so as not to vary depth of field appreciably. Unlike lead lanthanum zirconate titanate crystal, spatial light modulator does not require high voltage.

Gutow, David A.↗
NASA NTRS1990

Autonomous robot using infrared thermal camera to discriminate objects in outdoor scene

A complete autonomous legged robot is beig designed at Carnegie Mellon University to perform planetary exploration without human supervision. This robot must traverse unknown and geographically diverse areas in order to collect samples of materials. This paper describes how thermal imaging can be used to identify materials in order to find good footfall positions and collection sites of material. First, a model developed for determining the temperature of materials in an outdoor scene is presented. By applying this model, it is shown that it is possible to determine a physical characteristic of the material: thermal inertia. Second, experimental results are described that consist in recording thermal images of an outdoor scene constituted with sand and rock. Third, results and limitations of applying the model to experimental images are analyzed. Finally, the paper analyzes how basic segmentation algorithms can be combined with the thermal inertia segmentation in order to improve the discrimination of different kinds of materials.

Caillas, C.↗
NASA NTRS1991

Analysis of SSME inspection imagery using AI approaches

The automated analysis of SSME injector assemblies has been investigated for the cases of LOX post surface defects and injector-baffle deterioration. Defects are isolated via 2D feature extraction from borescope and camera images; temporal-frequency transforms are then used to create a multiresolution set of feature vectors representing image contents. The potential flaws thus discriminated are then segmented and classified according to known categories. AI is applied in the form of a blackboard architecture that is controlled by a rule-based production system.

Finegan, Michael K., Jr.↗
NASA NTRS2019

A CME-Producing Solar Eruption from the Interior of an Emerging Bipolar Active Region

In a negative-polarity coronal hole, magnetic flux emergence, seen by the Solar Dynamics Observatory's {SDO) Helioseismic Magnetic lmager (HMI), begins at approximately 19:00 UT on March 3, 2016. The emerged magnetic field produced sunspots, which NOAA numbered 12514 two days later. The emerging magnetic field is largely bipolar with the opposite-polarity fluxes spreading apart overall, but there is simultaneously some convergence and cancellation of opposite-polarity flux at the polarity inversion line (PIL) inside the emerging bipole. In the first fifteen hours after emergence onset, three obvious eruptions occur, observed in the coronal EUV images from SDO's Atmospheric Imaging Assembly (AIA). The first two erupt from separate segments of the external PIL between the emerging positve-polarity flux and the extant surrounding negative-polarity flux, with the exploding magnetic field being prepared and triggered by flux cancellation at the external PIL. The emerging bipole shows obvious overall left-handed shear and/or twist in its magnetic field. The focus of th is poster is the third and largest eruption, which comes from inside the emerging bipole and blows it open to produce a CME observed by SOHO/LASCO. That eruption is preceded by flux cancellation at the emerging bipole's interior PIL, cancellation that plausibly builds a sheared and twisted flux rope above the interior PIL and finally triggers the blow-out eruption of the flux rope via photospheric-convection•driven slow tether-cutting reconnection of the legs of the sheared core field, low above the interior PIL, as proposed by van Ballegooijen & Martens (1989) and Moore & Roumeliotis (1992). The production of this eruption is a (perhaps rare) counterexample to solar eruptions that result from external collisional shearing between opposite polarities from two distinct emerging and/or emerged bipoles (Chintzoglou et al. 2019).

Adams, M. L.↗
NASA NTRS2022

New Rover Conops with High-Performance Onboard Computing: Give Up Raw Data to Reduce Ops Cost and Do More Science

A major portion of time during the tactical operation of Mars rovers is spent for selecting, prioritizing, and coordinating sciences and engineering activities such that they fit within resource constraints, including the downlink data volume, energy, and time. In particular, the downlink data volume constraint is getting particularly tighter in recent missions because modern instruments produce increasingly high data volume while the communication bandwidth is essentially bounded by the law of physics. Tactical operation would be substantially simplified, hence the operation cost could be reduced, if the data volume constraint is relaxed or even removed. In this abstract, we propose a new operation paradigm for achieving this goal. The key observation is that, both in science and engineering applications, the bit size of raw data is typically much greater than the volume of processed information that is needed for scientific or engineering analysis. For example, a full-resolution image from Mastcam-Z, the main science camera on Perseverance, is about 700 kB in volume and we downlinked 29,685 images up to Sol 243, totaling ~20 GB of data. But of course, scientists do not use every pixel of these images; what they really look for in the images are geological features, typically represented by specific geometric configurations or textures. An end product after processing hundreds of Mascam-Z images could be a single geological map summarizing the spatial distribution of the features. For another example, a 100-meter drive of Perseverance produces 7-12 MB of drive telemetry, which records every detail of the rover's motion at 8 Hz, including position, attitude, steering angles, encoder readings, motor currents and many other information. But what the ground engineers eventually pay attention to is the signs of anomaly, such as excessive motor currents or high slip; if a drive is nominal, the vast majority of this data is unused. What if, then, we process the raw data onboard and only downlink the processed data that is relevant to scientific or engineering analyses, such as a list of detected science features (with cropped images) or a list of potential signs of anomaly while driving? A major roadblock for such onboard, high-level information processing has been the onboard computational resource. RAD750, the main onboard computer of Perseverance, is obviously not sufficient for performing complex image or signal processing such as object detection, semantic segmentation, or anomaly detection. Interestingly, RAD750 is not the best processor that Perseverance has; Qualcomm's Snapdragon 801, a modern mobile processor, is on her Heli Base Station, a device for communicating with Mars Helicopter Ingenuity; also, Intel's Atom E3845 processors are on engineering cameras. In the reminder of this paper, we will introduce two particular uses cases of these high-performance co-processors (meaning auxiliary CPU, GPU, or other types of processors that are separate from the main processor that runs the main flight software) for lowering operation cost and accommodating more science activities for a given communication constraint.

Didier, A.↗
NASA NTRS2024

X-Ray Polarized View of the Accretion Geometry in the X-Ray Binary Circinus X-1

Cir X-1 is a neutron star X-ray binary characterized by strong variations influx during its eccentric∼16.6 day orbit. There are also strong variations in the spectral state, and it has historically shown both atoll and Z state properties. We observed the source with the Imaging X-ray Polarimetry Explorer during two orbital segments, 6days apart, for a total of 263 ks. We find an X-ray polarization degree in these segments of 1.6%±0.3% and1.4%±0.3% at polarization angles of 37°±5°and−12°±7°, respectively. Thus, we observed a rotation of the polarization angle by 49°±8°along the orbit. Because variations of accretion flow, and then of the hardness ratio, are expected during the orbit, we also studied the polarization binned in hardness ratio and found the polarization angle differing by 67°±11°between the lowest and highest values of the hardness ratio. We discuss possible interpretations of this result that could indicate a possible misalignment between the symmetry axes of the accretion disk and the Comptonizing region caused by the misalignment of the neutron star’s angular momentum with respect to the orbital one.

Sco-X-1↗