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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Diffraction Modeling of Finite Subband EFC Probing on Dark Hole Contrast with WFIRST-CGI Shaped Pupil Coronagraph

The current NASA WFIRST (Wide-Field InfraRed Survey Telescope) coronagraph instrument (CGI) design allocates two subband filters per each full science band in order to contain system complexity and cost. We present our detailed investigation results on the adequacy of such a limited number of finite subband filters in achieving broadband dark hole contrast with a shaped pupil coronagraph (SPC). The study is based on diffraction propagation modeling with realistic WFIRST optics, where each subband’s image plane electric field is estimated from pairwise pupil plane deformable mirror (DM) probing and image plane intensity averaging of the resulting fields of multiple (subband) wavelengths. Multiple subband choices and probing and control strategies are explored, including standard subband probing; mixed wavelength and/or weighted Jacobian matrix; subband probing with intensity subtraction; and extended subband probing with intensity subtraction. Overall, the investigation shows that the achievable contrast with a limited number of finite subbands is about 2~2.5x worse than the designed contrast for the current SPC. The result suggests that future shaped pupil designs should be optimized for slightly broader bandwidths than the intended science bandpasses if limited subbands are used for wavefront sensing via probing.

WFIRST↗
NASA NTRSDate not supplied

Modeling EUV Intensity at the Top of the Transition Region Using SPICE Data on Board Solar Orbiter

The Spectral Imaging of Coronal Environment (SPICE; SPICE Consortium et al. 2020) provides an extraordinary opportunity to study the chromosphere and transition region using EUV wavelengths, e.g., Ne VIII 770 Å, CIII 977 Å, OVI 1032 Å, and Lyman-𝛽 025 Å. We present results modeling Ne VIII 770 Å intensity using images from SPICE and the COronal DEnsity and Temperature (CODET) model. This model is based on relationships between the magnetic field, density, and temperature. It uses a flux transport model, the Potential Field Extrapolation model (PFSS), an emission model based on Chianti atomic database 10.0.2, and an optimization algorithm. In addition, we assume that the emission from the top of the transition region (Ne VIII 770 Å) can be described using the magnetic field in the coronal base at 1.014𝑅⨀ (from PFSS). We found the mean full disc electron density in the transition region obtained from the CODET model for Ne VIII 770 Å (𝑙𝑜𝑔10𝑇[𝐾] = 5.8) corresponds to ∼ 𝑙𝑜𝑔10𝑁 𝑐𝑚−3 >9, and temperature value is 𝑙𝑜𝑔10𝑇[𝐾]~6. This value agrees with the Ne VIII 770 Å temperature formation 𝑙𝑜𝑔10𝑇[𝐾]~5.8. The density above sunspots (𝐵 > 50 𝐺) corresponds to 𝑙𝑜𝑔10𝑁 𝑐𝑚−3 > 9, while in quiet Sun regions (𝐵 < 50 𝐺) the mean density is 𝑙𝑜𝑔10𝑁[𝑐𝑚−3 ] = 8 .

Jenny M Rodriguez Gomez↗
DOE OSTI2013

Gaussian Multiple Instance Learning Approach for Mapping the Slums of the World Using Very High Resolution Imagery

In this paper, we present a computationally efficient algo- rithm based on multiple instance learning for mapping infor- mal settlements (slums) using very high-resolution remote sensing imagery. From remote sensing perspective, infor- mal settlements share unique spatial characteristics that dis- tinguish them from other urban structures like industrial, commercial, and formal residential settlements. However, regular pattern recognition and machine learning methods, which are predominantly single-instance or per-pixel classi- fiers, often fail to accurately map the informal settlements as they do not capture the complex spatial patterns. To overcome these limitations we employed a multiple instance based machine learning approach, where groups of contigu- ous pixels (image patches) are modeled as generated by a Gaussian distribution. We have conducted several experi- ments on very high-resolution satellite imagery, represent- ing four unique geographic regions across the world. Our method showed consistent improvement in accurately iden- tifying informal settlements.

Vatsavai, Raju↗
DOE OSTI2015

FESTR, Version 0.x

FESTR is a modeling tool for calculating spectroscopic-quality synthetic outputs (e.g., spectra and images) based on (in general) the time evolutions of 3-D spatial distributions of materials characterized by their chemical composition, temperature, and density conditions. FESTR performs raytracing across unstructured meshes, builds intrinsic optical properties of mixed materials by performing equation-of-state lookups in external data, and then solves the radiation-transport equation along rays to obtain emergent time and space resolved synthetic spectra. These results can be further processed into time and/or space integrated spectra, as well as into spectral-band-integrated synthetic images, radiated power histories, and total radiated energy values. FESTR is also applicable to spatial reconstructions of material properties via the analysis of experimental data.

Hakel, Peter↗
DOE OSTI2021

Influence of dissolved salts and impurities on seawater on heat transfer and fluid flow through fuel channels and debris bed

This project aims to better understand this mechanism and its effect on heat transfer. Specifically, experimental research was conducted to investigate the long term coolability issues in scaled test setups. Advanced diagnostic and measurement techniques were investigated and incorporated into an annular test facility and a debris bed test facility. The diagnostic techniques investigated included: radioactively tagged salt solution, Frisch collar radiation detector array, neutron and x-ray imaging systems, and a Rayleigh backscatter-based fiber-optic temperature system. Models were checked against the in-situ experimental data and recommendations were made for modeling purposes.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Machine learning pipeline for denoising low signal-to-noise ratio and out-of-distribution transmission electron microscopy datasets

High-resolution transmission electron microscopy (HRTEM) is crucial for observing material’s structural and morphological evolution at Angstrom scales, but the electron beam can alter these processes. Devices such as CMOS-based direct-electron detectors operating in electron-counting mode can be utilized to substantially reduce the electron dosage. However, the resulting images often lead to a low signal-to-noise ratio, which requires frame integration that sacrifices temporal resolution. Several machine learning (ML) models have been recently developed to successfully denoise HRTEM images. Yet, these models are often computationally expensive, and their inference speeds on GPUs are outpaced by the imaging speed of advanced detectors, precluding in situ analysis. Furthermore, the performance of these denoising models on datasets with imaging conditions that deviate from the training datasets has not been evaluated. To mitigate these gaps, we propose a new self-supervised ML denoising pipeline specifically designed for time-series HRTEM images. This pipeline integrates a blind-spot convolution neural network with pre-processing and post-processing steps, including drift correction and low-pass filtering. Results demonstrate that our model outperforms various other ML and non-ML denoising methods in noise reduction and contrast enhancement, leading to improved visual clarity of atomic features. Additionally, the model is drastically faster than U-Net-based ML models and demonstrates excellent out-of-distribution generalization. The model’s computational inference speed is in the order of milliseconds per image, rendering it suitable for application in in-situ HRTEM experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Exploring Gradient-Based Multi-directional Controls in GANs

Generative Adversarial Networks (GANs) have been widely applied in modeling diverse image distributions. However, despite its impressive applications, the structure of the latent space in GANs largely remains as a black-box, leaving its controllable generation an open problem, especially when spurious correlations between different semantic attributes exist in the image distributions. To address this problem, previous methods typically learn linear directions or individual channels that control semantic attributes in the image space. However, they often suffer from imperfect disentanglement, or are unable to obtain multi-directional controls. Furthermore, in this work, in light of the above challenges, we propose a novel approach that discovers nonlinear controls, which enables multi-directional manipulation as well as effective disentanglement, based on gradient information in the learned GAN latent space. More specifically, we first learn interpolation directions by following the gradients from classification networks trained separately on the attributes, and then navigate the latent space by exclusively controlling channels activated for the target attribute in the learned directions. Empirically, with small training data, our approach is able to gain fine-grained controls over a diverse set of bi-directional and multi-directional attributes, and we showcase its ability to achieve disentanglement significantly better than state-of-the-art methods both qualitatively and quantitatively.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Model-independent measurement of the charge density distribution along an Fe atom probe needle using off-axis electron holography without mean inner potential effects

The one-dimensional charge density distribution along an electrically biased Fe atom probe needle is measured using a model-independent approach based on off-axis electron holography in the transmission electron microscope. Both the mean inner potential and the magnetic contribution to the phase shift are subtracted by taking differences between electron-optical phase images recorded with different voltages applied to the needle. The measured one-dimensional charge density distribution along the needle is compared with a similar result obtained using model-based fitting of the phase shift surrounding the needle. On the assumption of cylindrical symmetry, it is then used to infer the three-dimensional electric field and electrostatic potential around the needle with ∼10 nm spatial resolution, without needing to consider either the influence of the perturbed reference wave or the extension of the projected potential outside the field of view of the electron hologram. The present study illustrates how a model-independent approach can be used to measure local variations in charge density in a material using electron holography in the presence of additional contributions to the phase, such as those arising from changes in mean inner potential and specimen thickness.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Validating and improving CT ventilation imaging by correlating with ventilation 4D-PET/CT using {sup 68}Ga-labeled nanoparticles

Purpose: CT ventilation imaging is a novel functional lung imaging modality based on deformable image registration. The authors present the first validation study of CT ventilation using positron emission tomography with{sup 68}Ga-labeled nanoparticles (PET-Galligas). The authors quantify this agreement for different CT ventilation metrics and PET reconstruction parameters. Methods: PET-Galligas ventilation scans were acquired for 12 lung cancer patients using a four-dimensional (4D) PET/CT scanner. CT ventilation images were then produced by applying B-spline deformable image registration between the respiratory correlated phases of the 4D-CT. The authors test four ventilation metrics, two existing and two modified. The two existing metrics model mechanical ventilation (alveolar air-flow) based on Hounsfield unit (HU) change (V{sub HU}) or Jacobian determinant of deformation (V{sub Jac}). The two modified metrics incorporate a voxel-wise tissue-density scaling (ρV{sub HU} and ρV{sub Jac}) and were hypothesized to better model the physiological ventilation. In order to assess the impact of PET image quality, comparisons were performed using both standard and respiratory-gated PET images with the former exhibiting better signal. Different median filtering kernels (σ{sub m} = 0 or 3 mm) were also applied to all images. As in previous studies, similarity metrics included the Spearman correlation coefficient r within the segmented lung volumes, and Dice coefficient d{sub 20} for the (0 − 20)th functional percentile volumes. Results: The best agreement between CT and PET ventilation was obtained comparing standard PET images to the density-scaled HU metric (ρV{sub HU}) with σ{sub m} = 3 mm. This leads to correlation values in the ranges 0.22 ⩽ r ⩽ 0.76 and 0.38 ⩽ d{sub 20} ⩽ 0.68, with r{sup ¯}=0.42±0.16 and d{sup ¯}{sub 20}=0.52±0.09 averaged over the 12 patients. Compared to Jacobian-based metrics, HU-based metrics lead to statistically significant improvements in r{sup ¯} and d{sup ¯}{sub 20} (p < 0.05), with density scaled metrics also showing higher r{sup ¯} than for unscaled versions (p < 0.02). r{sup ¯} and d{sup ¯}{sub 20} were also sensitive to image quality, with statistically significant improvements using standard (as opposed to gated) PET images and with application of median filtering. Conclusions: The use of modified CT ventilation metrics, in conjunction with PET-Galligas and careful application of image filtering has resulted in improved correlation compared to earlier studies using nuclear medicine ventilation. However, CT ventilation and PET-Galligas do not always provide the same functional information. The authors have demonstrated that the agreement can improve for CT ventilation metrics incorporating a tissue density scaling, and also with increasing PET image quality. CT ventilation imaging has clear potential for imaging regional air volume change in the lung, and further development is warranted.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS2008

Software Simulates Sight: Flat Panel Mura Detection

In the increasingly sophisticated world of high-definition flat screen monitors and television screens, image clarity and the elimination of distortion are paramount concerns. As the devices that reproduce images become more and more sophisticated, so do the technologies that verify their accuracy. By simulating the manner in which a human eye perceives and interprets a visual stimulus, NASA scientists have found ways to automatically and accurately test new monitors and displays. The Spatial Standard Observer (SSO) software metric, developed by Dr. Andrew B. Watson at Ames Research Center, measures visibility and defects in screens, displays, and interfaces. In the design of such a software tool, a central challenge is determining which aspects of visual function to include while accuracy and generality are important, relative simplicity of the software module is also a key virtue. Based on data collected in ModelFest, a large cooperative multi-lab project hosted by the Optical Society of America, the SSO simulates a simplified model of human spatial vision, operating on a pair of images that are viewed at a specific viewing distance with pixels having a known relation to luminance. The SSO measures the visibility of foveal spatial patterns, or the discriminability of two patterns, by incorporating only a few essential components of vision. These components include local contrast transformation, a contrast sensitivity function, local masking, and local pooling. By this construction, the SSO provides output in units of "just noticeable differences" (JND) a unit of measure based on the assumed smallest difference of sensory input detectable by a human being. Herein is the truly amazing ability of the SSO, while conventional methods can manipulate images, the SSO models human perception. This set of equations actually defines a mathematical way of working with an image that accurately reflects the way in which the human eye and mind behold a stimulus. The SSO is intended for a wide variety of applications, such as evaluating vision from unmanned aerial vehicles, measuring visibility of damage to aircraft and to the space shuttles, predicting outcomes of corrective laser eye surgery, inspecting displays during the manufacturing process, estimating the quality of compressed digital video, evaluating legibility of text, and predicting discriminability of icons or symbols in a graphical user interface.

Source record↗
NASA NTRS2022

Terrestrial Exospheric Dayside H-density Profile at 3 - 15 RE from UVIS/HDAC and TWINS Lyman-alpha Data Combined

Terrestrial ecliptic dayside observations of the exospheric Lyman-α column intensity between 3–15 Earth radii (RE) by UVIS/HDAC (UVIS – ultraviolet imaging spectrograph; HDAC – hydrogen-deuterium absorption cell) Lyman-α photometer at CASSINI have been analyzed to derive the neutral exospheric H-density profile at the Earth’s ecliptic dayside in this radial range. The data were measured during CASSINI’s swing-by maneuver at the Earth on 18 August 1999 and are published by Werner et al. (2004). In this study the dayside HDAC Lyman-α observations published by Werner et al. (2004) are compared to calculated Lyman-α intensities based on the 3D H-density model derived from TWINS (Two Wide-angle Imaging Neutral-atom Spectrometers) Lyman-α observations between 2008–2010 (Zoennchen et al., 2015). It was found that both Lyman-α profiles show a very similar radial dependence in particular between 3–8RE. Between 3.0–5.5RE impact distance Lyman-α observations of both TWINS and UVIS/HDAC exist at the ecliptic dayside. In this overlapping region the cross-calibration of the HDAC profile against the calculated TWINS profile was done, assuming that the exosphere there was similar for both due to comparable space weather conditions. As a result of the cross-calibration the conversion factor between counts per second and rayleigh, fc = 3.285 counts s−1R−1, is determined for these HDAC observations. Using this factor, the radial H-density profile for the Earth’s ecliptic dayside was derived from the UVIS/HDAC observations, which constrained the neutral H density there at 10RE to a value of 35 cm-3. Furthermore, a faster radial H-density decrease was found at distances above 8RE (≈r−2.37) compared to the lower distances of 3–7RE (≈r−2.37). This increased loss of neutral H above 8RE might indicate a higher rate of H ionization in the vicinity of the magnetopause at 9–11RE (near subsolar point) and beyond, because of increasing charge exchange interactions of exospheric H atoms with solar wind ions outside the magnetosphere.

Jochen H Zoennchen↗
NASA NTRS1983

Simulation studies and the need for field reflectance studies

There is a need to calibrate satellite data obtained at large polar (i.e., off-nadir) view angles for effects caused by scanner geometry and by the atmosphere. If such effects are not corrected for, then recorded radiance data will contain systematic and random errors which will make accurate target identification and quantification difficult. Adequate calibration of digital radiance data requires both an inductive analysis of digital satellite images and a deductive analysis based upon a priori simulation studies. The inputs to a simulation model (ground reflectance, atmospheric transmission and bandscatter) and their variability are dependent upon (for example) sun-target-sensor geometry. There is a major need to make accurate ground reflectance measurements to provide calibration information on the anisotropy of ground reflectance. There is also a need to assess atmospheric transmission and backscatter from the image itself. A potential simple, inexpensive method for making ground reflectance measurements in the above context is discussed.

Duggin, M. J.↗
NASA NTRS2012

Iterative Nonlinear Tikhonov Algorithm with Constraints for Electromagnetic Tomography

Low frequency electromagnetic tomography such as the capacitance tomography (ECT) has been proposed for monitoring and mass-gauging of gas-liquid two-phase system under microgravity condition in NASA's future long-term space missions. Due to the ill-posed inverse problem of ECT, images reconstructed using conventional linear algorithms often suffer from limitations such as low resolution and blurred edges. Hence, new efficient high resolution nonlinear imaging algorithms are needed for accurate two-phase imaging. The proposed Iterative Nonlinear Tikhonov Regularized Algorithm with Constraints (INTAC) is based on an efficient finite element method (FEM) forward model of quasi-static electromagnetic problem. It iteratively minimizes the discrepancy between FEM simulated and actual measured capacitances by adjusting the reconstructed image using the Tikhonov regularized method. More importantly, it enforces the known permittivity of two phases to the unknown pixels which exceed the reasonable range of permittivity in each iteration. This strategy does not only stabilize the converging process, but also produces sharper images. Simulations show that resolution improvement of over 2 times can be achieved by INTAC with respect to conventional approaches. Strategies to further improve spatial imaging resolution are suggested, as well as techniques to accelerate nonlinear forward model and thus increase the temporal resolution.

Xu, Feng↗
NASA NTRSDate not supplied

Validation of Image-Based Neural Network Controllersthrough Adaptive Stress Testing

Neural networks have become state-of-the-art for computer vision problems because of their ability to efficiently model complex functions from large amounts of data. While neural networks can be shown to perform well empirically fora variety of tasks, their performance is difficult to guarantee.Neural network verification tools have been developed that can certify robustness with respect to a given input image; however,for neural network systems used in closed-loop controllers,robustness with respect to individual images does not address multi-step properties of the neural network controller and itsenvironment. Furthermore, neural network systems interacting in the physical world and using natural images are operating in a black-box environment, making formal verification in-tractable. This work combines the adaptive stress testing (AST)framework with neural network verification tools to search for the most likely sequence of image disturbances that cause the neural network controlled system to reach a failure. Anautonomous aircraft taxi application is presented, and results show that the AST method finds failures with more likely image disturbances than baseline methods. Further analysis of AST results revealed an explainable cause of the failure, giving insight into the problematic scenarios that should be addressed.

Adaptive Stress Testing, Marabou, Deep Neural Netw↗
DOE OSTI2016

Online coupled camera pose estimation and dense reconstruction from video

A product may receive each image in a stream of video image of a scene, and before processing the next image, generate information indicative of the position and orientation of an image capture device that captured the image at the time of capturing the image. The product may do so by identifying distinguishable image feature points in the image; determining a coordinate for each identified image feature point; and for each identified image feature point, attempting to identify one or more distinguishable model feature points in a three dimensional (3D) model of at least a portion of the scene that appears likely to correspond to the identified image feature point. Thereafter, the product may find each of the following that, in combination, produce a consistent projection transformation of the 3D model onto the image: a subset of the identified image feature points for which one or more corresponding model feature points were identified; and, for each image feature point that has multiple likely corresponding model feature points, one of the corresponding model feature points. The product may update a 3D model of at least a portion of the scene following the receipt of each video image and before processing the next video image base on the generated information indicative of the position and orientation of the image capture device at the time of capturing the received image. The product may display the updated 3D model after each update to the model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Novel deep learning methods for track reconstruction

For the past year, the HEP.TrkX project has been investigating machine learning solutions to LHC particle track reconstruction problems. A variety of models were studied that drew inspiration from computer vision applications and operated on an image-like representation of tracking detector data. While these approaches have shown some promise, image-based methods face challenges in scaling up to realistic HL-LHC data due to high dimensionality and sparsity. In contrast, models that can operate on the spacepoint representation of track measurements ("hits") can exploit the structure of the data to solve tasks efficiently. In this paper we will show two sets of new deep learning models for reconstructing tracks using space-point data arranged as sequences or connected graphs. In the first set of models, Recurrent Neural Networks (RNNs) are used to extrapolate, build, and evaluate track candidates akin to Kalman Filter algorithms. Such models can express their own uncertainty when trained with an appropriate likelihood loss function. The second set of models use Graph Neural Networks (GNNs) for the tasks of hit classification and segment classification. These models read a graph of connected hits and compute features on the nodes and edges. They adaptively learn which hit connections are important and which are spurious. The models are scaleable with simple architecture and relatively few parameters. Results for all models will be presented on ACTS generic detector simulated data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1992

Application of the multiple PRF technique to resolve Doppler centroid estimation ambiguity for spaceborne SAR

Estimation of the Doppler centroid ambiguity is a necessary element of the signal processing for SAR systems with large antenna pointing errors. Without proper resolution of the Doppler centroid estimation (DCE) ambiguity, the image quality will be degraded in the system impulse response function and the geometric fidelity. Two techniques for resolution of DCE ambiguity for the spaceborne SAR are presented; they include a brief review of the range cross-correlation technique and presentation of a new technique using multiple pulse repetition frequencies (PRFs). For SAR systems, where other performance factors control selection of the PRF's, an algorithm is devised to resolve the ambiguity that uses PRF's of arbitrary numerical values. The performance of this multiple PRF technique is analyzed based on a statistical error model. An example is presented that demonstrates for the Shuttle Imaging Radar-C (SIR-C) C-band SAR, the probability of correct ambiguity resolution is higher than 95 percent for antenna attitude errors as large as 3 deg.

Chang, C. Y.↗
DOE OSTI2020

Modeling the Transmission of Optical Lightning Signals Through Complex 3‐D Cloud Scenes

Abstract Space‐based lightning imagers have shown that complex cloud scenes consisting of multiple tall convective features, anvil clouds, and warm boundary cloud layers are illuminated by lightning in many different ways depending on where the lightning occurs and how energetic it is. Modifications to the optical lightning signals from radiative transfer in the cloud medium can lead to reductions in detection efficiency and location accuracy for these instruments and can also cause some of the optical signals that are detected to have unexpected spatial energy distributions. In this study, we perform Monte Carlo radiative transfer simulations of optical lightning emissions in clouds with complex 3‐D geometries to shed some light on the origins of certain irregular radiance patterns that have been recorded from orbit. We show that diffuse reflections off nearby cloud faces can explain lightning signals in nonelectrified clouds, tall clouds can result in poor optical transmission and suppressed radiances that could lead to missed events and that particularly favorable viewing conditions can cause otherwise normal lightning to produce a superbolt that is orders of magnitude brighter than the same flash seen from a different angle.

54 ENVIRONMENTAL SCIENCES↗