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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Improving OFDR Distributed Fiber Sensing by Fibers With Enhanced Rayleigh Backscattering and Image Processing

This article investigates using optical fibers with enhanced backscattering profiles to improve distributed fiber sensor performance and reduce instrumentation costs. Using a femtosecond (fs) laser direct writing technique, the Rayleigh backscattering profile of a standard telecom fiber was enhanced by more than 40 dB to improve the signal-to-noise ratio (SNR) for optical frequency domain reflectometry (OFDR). The enhanced backscattering signals enable effectively distributed strain measurements using a low-cost tunable laser (TL). Median filtering is applied to denoise cross correlation results to further improve measurement outcomes. Results presented in this article show that a TL with a 1-nm tuning range, which is far less than the tuning range used in commercial OFDR interrogators, can perform effectively distributed strain measurements using sensing fibers with enhanced backscattering profiles. The sensing fiber with over 40-dB backscattering enhancement achieved 4.8-cm spatial resolution in strain measurements with a root mean square accuracy of less than 2.70 με when 10–50 με were exerted to the sensing fiber. In conclusion, results presented in this article reveal both the potential and limitations of sensing fibers with enhanced backscattering for OFDR-based distributed fiber sensors.

42 ENGINEERING↗
DOE OSTI2021

Unsupervised Azimuth Estimation of Solar Arrays in Low-Resolution Satellite Imagery through Semantic Segmentation and Hough Transform

This paper explains the use of a convolutional neural network (CNN) to segment solar panels in a satellite image containing solar arrays, and extract associated metadata from the arrays. A novel unsupervised technique is introduced to estimate the azimuth of each individual solar panel from the predicted mask of the convolutional neural network. This pipeline was developed with the aim of extracting necessary metadata for a solar installation, using only a set of latitude–longitude coordinates. Azimuth prediction results for 669 individual solar installations associated with 387 sites located across the United States are provided. A mean average error and median average error of 21.65 degrees and 1.0 degrees were obtained, respectively, when predicting the azimuth of the solar fleet data set, with about 80% of the results within an error of zero degrees of the ground truth azimuth value and about 85% within an error of 25 degrees. The predicted azimuth was then used to estimate the energy conversion of the solar arrays. Results show a 90.9 and 90.6 R-squared value for estimating alternating current (AC) and direct current (DC) energy, respectively, and a mean absolute percentage error (MAPE) of 1.70% in estimating the alternating current (AC) energy using the fully automated algorithm.

14 SOLAR ENERGY↗
DOE OSTI2020

Deep medical image analysis with representation learning and neuromorphic computing

Deep learning is increasingly used in medical imaging, improving many steps of the processing chain, from acquisition to segmentation and anomaly detection to outcome prediction. Yet significant challenges remain: (i) image-based diagnosis depends on the spatial relationships between local patterns, something convolution and pooling often do not capture adequately; (ii) data augmentation, the de facto method for learning three-dimensional pose invariance, requires exponentially many points to achieve robust improvement; (iii) labelled medical images are much less abundant than unlabelled ones, especially for heterogeneous pathological cases; and (iv) scanning technologies such as magnetic resonance imaging can be slow and costly, generally without online learning abilities to focus on regions of clinical interest. To address these challenges, novel algorithmic and hardware approaches are needed for deep learning to reach its full potential in medical imaging.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

An Application of Faster R-CNNs in Object Detection Efforts Applied to the Critical Infrastructure Domain

Object detection and segmentation approaches have been successfully applied to multiple interdisciplinary domains ranging from medical imaging to autonomous vehicles. The following study details efforts to apply segmentation methods to the critical infrastructure domain. Specifically, the identification of critical sub-components of four facilities: airports, hospitals, hydroelectric dams, and water treatment plants. Data for this study was extracted from the U.S. Department of Agriculture’s National Agriculture Imagery Program data set. Each facility class was considered independently of the other facilities, producing four different segmentation models during the testing and training process. Ultimately, the study team concluded there are significant challenges associated with object detection modeling when applied to facility analysis within the critical infrastructure domain. The study team was able to identify possible sources of these challenges that could be potentially addressed in future work.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2014

Three-Dimensional Imaging and Numerical Reconstruction of Graphite/Epoxy Composite Microstructure Based on Ultra-High Resolution X-Ray Computed Tomography

A combined experimental and computational study aimed at high-resolution 3D imaging, visualization, and numerical reconstruction of fiber-reinforced polymer microstructures at the fiber length scale is presented. To this end, a sample of graphite/epoxy composite was imaged at sub-micron resolution using a 3D X-ray computed tomography microscope. Next, a novel segmentation algorithm was developed, based on concepts adopted from computer vision and multi-target tracking, to detect and estimate, with high accuracy, the position of individual fibers in a volume of the imaged composite. In the current implementation, the segmentation algorithm was based on Global Nearest Neighbor data-association architecture, a Kalman filter estimator, and several novel algorithms for virtualfiber stitching, smoothing, and overlap removal. The segmentation algorithm was used on a sub-volume of the imaged composite, detecting 508 individual fibers. The segmentation data were qualitatively compared to the tomographic data, demonstrating high accuracy of the numerical reconstruction. Moreover, the data were used to quantify a) the relative distribution of individual-fiber cross sections within the imaged sub-volume, and b) the local fiber misorientation relative to the global fiber axis. Finally, the segmentation data were converted using commercially available finite element (FE) software to generate a detailed FE mesh of the composite volume. The methodology described herein demonstrates the feasibility of realizing an FE-based, virtual-testing framework for graphite/fiber composites at the constituent level.

Czabaj, M. W.↗
DOE OSTI2023

Methods for rapid identification of anomalous layers in laser powder bed fusion

In situ process monitoring is a key requirement for increased industry acceptance of powder bed Additive Manufacturing. As sensing technologies increase in maturity, attention must also be given to effective data exploration techniques. These data are often high-resolution and multi-modal, with each build consisting of thousands of layers. Here, in this paper, the authors propose two methods enabling users to rapidly identify layers of interest within a build. Both methods leverage results from deep learning based segmentations of in situ powder bed images. The first method is an unsupervised “reverse layer search” algorithm while the second method uses supervised machine learning.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Search for very-short-baseline oscillations of reactor antineutrinos with the SoLid detector

In this paper we report the first scientific result based on antineutrinos emitted from the BR2 reactor at SCK CEN. The SoLid experiment uses a novel type of highly granular detector whose basic detection unit combines two scintillators, polyvinyl toluene (PVT) and Li 6 F : ZnS ( Ag ) , to measure antineutrinos via their inverse- β -decay products. An advantage of PVT is its highly linear response as a function of deposited particle energy. The full-scale detector comprises 12 800 voxels and operates over a very short 6.3–8.9 m baseline from the reactor core. The detector segmentation and its three-dimensional imaging capabilities facilitate the extraction of the positron energy from the rest of the visible energy, allowing the latter to be utilized for signal-background discrimination. We present a result obtained from 280 reactor-on days (55 MW mean power) and 172 reactor-off days, respectively, of live data taking. A total of 29 479 ± 603 (stat) antineutrino candidates have been selected, corresponding to an average rate of 105 events per day and a signal-to-background ratio of 0.27. A search for disappearance of antineutrinos to a sterile state has been conducted using complementary model-dependent frequentist and Bayesian fits, providing constraints on the allowed region of the reactor antineutrino anomaly.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Compact single-shot soft X-ray photon spectrometer for free-electron laser diagnostics

The photon spectrum from free-electron laser (FEL) light sources offers valuable information in time-resolved experiments and machine optimization in the spectral and temporal domains. We have developed a compact single-shot photon spectrometer to diagnose soft X-ray spectra. The spectrometer consists of an array of off-axis Fresnel zone plates (FZP) that act as transmission-imaging gratings, a Ce:YAG scintillator, and a microscope objective to image the scintillation target onto a two-dimensional imaging detector. This spectrometer operates in segmented energy ranges which covers tens of electronvolts for each absorption edge associated with several atomic constituents: carbon, nitrogen, oxygen, and neon. The spectrometer’s performance is demonstrated at a repetition rate of 120 Hz, but our detection scheme can be easily extended to 200 kHz spectral collection by employing a fast complementary metal oxide semiconductor (CMOS) line-scan camera to detect the light from the scintillator. This compact photon spectrometer provides an opportunity for monitoring the spectrum downstream of an endstation in a limited space environment with sub-electronvolt energy resolution.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Addressing Issues with Working Memory in Video Object Segmentation

Contemporary state-of-the-art video object segmentation (VOS) models compare incoming unannotated images to a history of image-mask relations via affinity or cross-attention to predict object masks. We refer to the internal memory state of the initial image-mask pair and past image-masks as a working memory buffer. While the current state of the art models perform very well on clean video data, their reliance on a working memory of previous frames leaves room for error. Affinity-based algorithms include the inductive bias that there is temporal continuity between consecutive frames. To account for inconsistent camera views of the desired object, working memory models need an algorithmic modification that regulates the memory updates and avoid writing irrelevant frames into working memory. A simple algorithmic change is proposed that can be applied to any existing working memory-based VOS model to improve performance on inconsistent views, such as sudden camera cuts, frame interjections, and extreme context changes. The resulting model performances show significant improvement on video data with these frame interjections over the same model without the algorithmic addition. Our contribution is a simple decision function that determines whether working memory should be updated based on the detection of sudden, extreme changes and the assumption that the object is no longer in frame. By implementing algorithmic changes, such as this, we can increase the real-world applicability of current VOS models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Red Wine Fermentation Alters Grape Seed Morphology and Internal Porosity

During wine fermentation, grape berry components, including seeds, undergo extensive physical and chemical changes that result in the release of flavonoids, such as tannins, from seeds into wine. Understanding changes in seed morphology during fermentation is crucial for aiding the development of accurate prediction models for flavonoid extraction during winemaking, which enhances fermentation management and ensures consistency in wines from year to year. High-resolution x-ray microcomputed tomography (x-ray μCT) was used to investigate the effect of red wine fermentation on changes in grape seed morphology. Using a PyTorch-based implementation of a fully convolutional network with a Resnet-101 backbone for semantic segmentation of x-ray μCT images, we quantified extensive alteration to grape seed structure during fermentation. Image analyses revealed the development of a pore network breaking apart the seed endosperm by the end of fermentation, leading to an increase in surface area. Fermentation significantly altered grape seed morphology. Such alterations could enable transport of seed flavonoids from inside the endosperm and integument to outside the seed. Further research on the physical processes occurring in seeds during wine fermentation is necessary to build better physiochemical models.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Data supporting manuscript from L. Sheneman, G. Stephanopoulos, A.E. Vasdekis titled "Deep learning classification of lipid droplets in quantitative phase images" as currently under review at PLOS ONE. This includes: 1) raw and binary labeled Quantitative Phase Images (QPI) of Y. lipolytica cells used in the analyses described within the manuscript. 2) various derived data including classifier scores, etc.

Data supporting manuscript from L. Sheneman, G. Stephanopoulos, A.E. Vasdekis titled "Deep learning classification of lipid droplets in quantitative phase images" as currently under review at PLOS ONE. This includes: 1) raw and binary labeled Quantitative Phase Images (QPI) of Y. lipolytica cells used in the analyses described within the manuscript. 2) various derived data including classifier scores, etc.

ANN↗
NASA NTRS1990

Optical neural network system for pose determination of spinning satellites

An optical neural network architecture and algorithm based on a Hopfield optimization network are presented for multitarget tracking. This tracker utilizes a neuron for every possible target track, and a quadratic energy function of neural activities which is minimized using gradient descent neural evolution. The neural net tracker is demonstrated as part of a system for determining position and orientation (pose) of spinning satellites with respect to a robotic spacecraft. The input to the system is time sequence video from a single camera. Novelty detection and filtering are utilized to locate and segment novel regions from the input images. The neural net multitarget tracker determines the correspondences (or tracks) of the novel regions as a function of time, and hence the paths of object (satellite) parts. The path traced out by a given part or region is approximately elliptical in image space, and the position, shape and orientation of the ellipse are functions of the satellite geometry and its pose. Having a geometric model of the satellite, and the elliptical path of a part in image space, the three-dimensional pose of the satellite is determined. Digital simulation results using this algorithm are presented for various satellite poses and lighting conditions.

Lee, Andrew↗
NASA NTRS2003

Validating NASA's Airborne Multikilohertz Microlaser Altimeter (Microaltimeter) by Direct Comparison of Data Taken Over Ocean City, Maryland Against an Existing Digital Elevation Model

NASA's Airborne Multikilohertz Microlaser Altimeter (Microaltimeter) is a scanning, photon-counting laser altimeter, which uses a low energy (less than 10 microJuoles), high repetition rate (approximately 10 kHz) laser, transmitting at 532 nm. A 14 cm diameter telescope images the ground return onto a segmented anode photomultiplier, which provides up to 16 range returns for each fire. Multiple engineering flights were made during 2001 and 2002 over the Maryland and Virginia coastal area, all during daylight hours. Post-processing of the data to geolocate the laser footprint and determine the terrain height requires post- detection Poisson filtering techniques to extract the actual ground returns from the noise. Validation of the instrument's ability to produce accurate terrain heights will be accomplished by direct comparison of data taken over Ocean City, Maryland with a Digital Elevation Model (DEM) of the region produced at Ohio State University (OSU) from other laser altimeter and photographic sources. The techniques employed to produce terrain heights from the Microaltimeter ranges will be shown, along with some preliminary comparisons with the OSU DEM.

Abel, Peter↗
NASA NTRS2003

Toroidal Tank Development for Upper-stages

The advantages, development, and fabrication of toroidal propellant tanks are profiled in this viewgraph presentation. Several images are included of independent research and development (IR&D) of toroidal propellant tanks at Marshall Space Flight Center (MSFC). Other images in the presentation give a brief overview of Thiokol conformal tank technology development. The presentation describes Thiokol's approach to continuous composite toroidal tank fabrication in detail. Images are shown of continuous and segmented toroidal tanks fabricated by Thiokol.

DeLay, Tom↗
NASA NTRS2004

Automated Detection of Solar Loops by the Oriented Connectivity Method

An automated technique to segment solar coronal loops from intensity images of the Sun s corona is introduced. It exploits physical characteristics of the solar magnetic field to enable robust extraction from noisy images. The technique is a constructive curve detection approach, constrained by collections of estimates of the magnetic fields orientation. Its effectiveness is evaluated through experiments on synthetic and real coronal images.

Lee, Jong Kwan↗
DOE OSTI2026

Patch-Based Convolutional Neural Networks for Multiple Microstructural Features Detection in FIB-SEM Micrographs of Irradiated Nuclear Fuel

Focused ion beam scanning electron microscopy (FIB-SEM) tomography has increasingly been utilized for acquiring three-dimensional (3D) microstructure features at the sub-micron scale in irradiated nuclear materials. This technique involves sequential ion beam slicing followed by electron beam imaging and compositional mapping using energy dispersive spectroscopy (EDS). Despite its growing use, several challenges persist. These include the time-intensive nature of data collection of EDS data, difficulties in distinguishing between various microstructures, and issues with image alignment. These challenges currently limit the broader application of FIB-SEM tomography in the field. To overcome these limitations, we propose using convolutional neural networks (CNNs) to automate microstructure identification in SEM images. Our study introduces a new framework for identifying microstructures in irradiated U-10Zr (wt. %) metallic fuel with limited annotated data. The framework includes the creation of a reliable annotated dataset with paired SEM and ground truth data from EDS maps, the applications of CNNs for microstructure identification, and the validation of model performance. Specifically, we employed the Segment Anything Model (SAM) to align SEM images with corresponding EDS maps and focused ion beam (FIB) tomography SEM data. We evaluate several models, including Patch-based U-Net, Attention U-Net, and Residual U-Net, finding that patch-based U-Net exhibits superior segmentation performance and consistency. This approach reduces reliance on EDS detectors and aids in accelerating nuclear material analysis process, highlighting the potential of advanced deep learning techniques to improve microstructural understanding in nuclear material. This is the first framework to integrate SAM and Patch-based CNN models for semantic segmentation of irradiated nuclear materials, with potential applicability to other tomography datasets.

36 - MATERIALS SCIENCE↗
NASA NTRS1988

Real-time sensing of optical alignment

The Large Deployable Reflector and other future segmented optical systems may require autonomous, real-time alignment of their optical surfaces. Researchers have developed gratings located directly on a mirror surface to provide interferometric sensing of the location and figure of the mirror. The grating diffracts a small portion of the incident beam to a diffractive focus where the designed diagnostics can be performed. Mirrors with diffraction gratings were fabricated in two separate ways. The formation of a holographic grating over the entire surface of a mirror, thereby forming a Zone Plate Mirror (ZPM) is described. Researchers have also used computer-generated hologram (CGH) patches for alignment and figure sensing of mirrors. When appropriately illuminated, a grid of patches spread over a mirror segment will yield a grid of point images at a wavefront sensor, with the relative location of the points providing information on the figure and location of the mirror. A particular advantage of using the CGH approach is that the holographic patches can be computed, fabricated, and replicated on a mirror segment in a mass production 1-g clean room environment.

Stier, Mark T.↗