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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 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Measurement of Photovoltaic Module Deformation Dynamics During Hail Impact Using Digital Image Correlation

Stereo high-speed video of photovoltaic modules undergoing laboratory hail tests was processed using digital image correlation to determine module surface deformation during and immediately following impact. The purpose of this work was to demonstrate a methodology for characterizing module impact response differences as a function of construction and incident hail parameters. Video capture and digital image analysis were able to capture out-of-plane module deformation to a resolution of ±0.1 mm at 11 kHz on an in-plane grid of 10 × 10 mm over the area of a 1 × 2 m commercial photovoltaic module. With lighting and optical adjustments, the technique was adaptable to arbitrary module designs, including size, backsheet color, and cell interconnection. Furthermore, impacts were observed to produce an initially localized dimple in the glass surface, with peak deflection proportional to the square root of incident energy. Subsequent deformation propagation and dissipation were also captured, along with behavior for instances when the module glass fractured. Natural frequencies of the module were identifiable by analyzing module oscillations postimpact. Limitations of the measurement technique were that the impacting ice ball obscured the data field immediately surrounding the point of contact, and both ice and glass fracture events occurred within 100 μs, which was not resolvable at the chosen frame rate. Increasing the frame rate and visualizing the back surface of the impact could be applied to avoid these issues. Applications for these data include validating computational models for hail impacts, identifying the natural frequencies of a module, and identifying damage initiation mechanisms.

14 SOLAR ENERGY↗
DOE OSTI2019

Assessing XMT-Measurement Variability of Air-Water Interfacial Areas in Natural Porous Media

This study investigates the accuracy and reproducibility of air-water interfacial areas measured with high-resolution synchrotron X-ray microtomography (XMT). Columns packed with one of two relatively coarse-grained monodisperse granular media, glass beads or a well-sorted quartz sand, were imaged over several years, encompassing changes in acquisition equipment, improved image quality, and enhancements to image acquisition and to processing software. For the glass beads, the specific solid surface area (SSSA-XMT) of 31.6 ± 1 cm -1 determined from direct analysis of the segmented solid-phase image data is statistically identical to the independently calculated geometric smooth-sphere specific solid surface area (GSSA, 32 ± 1 cm -1 ) and to the measured SSSA (28 ± 3 cm -1 ) obtained with the N2-Brunauer, Emmett, and Teller method. The maximum specific air-water interfacial area (Amax) is 27.4 (±2) cm -1 , which compares very well to the SSSA-XMT, GSSA, and SSSA-N2-Brunauer, Emmett, and Teller values. For the sand, the SSSA-XMT (111 ± 2 cm -1 ) and GSSA (113 ± 1 cm -1 ) are similar. The mean Amax is 96 ± 5 cm -1 , which compares well to both the SSSA and the GSSA values. The XMT-SSSA values deviated from the GSSA values by 7–16% for the first four experiments but were essentially identical for the later experiments. This indicates that enhancements in image acquisition and processing improved data accuracy. The Amax values ranged from 74 cm -1 to 101 cm -1 , with a coefficient of variation (COV) of 9%. The maximum capillary interfacial area ranged from 12 cm-1 to 19 cm -1 , for a COV of 10%. The COVs for both decreased to 5–6% for the latter five experiments. These results demonstrate that XMT imaging provides accurate and reproducible measurements of total and capillary interfacial areas.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Integrative Quantitative-Phase and Airy Light-Sheet Imaging

Light-sheet microscopy enables considerable speed and phototoxicity gains, while quantitative-phase imaging confers label-free organelle recognition and metabolic information that are inaccessible by conventional methods. We report the fusion of these two modalities onto a standard inverted microscope that retains compatibility with microfluidics. We describe the utilization of an accelerating Airy-beam light-sheet yielding identical imaging areas with interferometry, and an application in unmasking the effects of cellular noise on metabolic compartmentalization.

Biological sciences, Biological techniques, Micros↗
DOE OSTI2024

HyperKube: A Kubernetes Based System for the Automation of Processing and Analysis of Hyperspectral Data Obtained from Multiple Hyperspectral Imaging Systems

Hyperspectral imagery is an emerging field of technology that has enormous potential for remote and proximal sensing in numerous areas of research. The plant phenotyping community is applying this technology to advance the throughput and accuracy of plant phenotypes based on airborne and lab-based hyperspectral imaging technology. Here we report an automated processing and analysis pipeline for four different hyperspectral imaging platforms, discuss the data issues involved, and present a strategy for computing and data architecture to handle hyperspectral data.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Measurement of hybrid rocket solid fuel regression rate for a slab burner using deep learning

This study presents an imaging-based deep learning tool to measure the fuel regression rate in a 2D slab burner experiment for hybrid rocket fuels. The slab burner experiment is designed to verify mechanistic models of reacting boundary layer combustion in hybrid rockets by the measurement of fuel regression rates. A DSLR camera with a high intensity flash is used to capture images throughout the burn and the images are then used to find the fuel boundary to calculate the regression rate. A U-net convolutional neural network architecture is explored to segment the fuel from the experimental images. Here, a Monte-Carlo Dropout process is used to quantify the regression rate uncertainty produced from the network. The U-net computed regression rates are compared with values from other techniques from literature and show error less than 10%. An oxidizer flux dependency study is performed and shows the U-net predictions of regression rates are accurate and independent of the oxidizer flux, when the images in the training set are not over-saturated. Training with monochrome images is explored and is not successful at predicting the fuel regression rate from images with high noise. The network is superior at filtering out noise introduced by soot, pitting, and wax deposition on the chamber glass as well as the flame when compared to traditional image processing techniques, such as threshold binary conversion and spatial filtering. U-net consistently provides low error image segmentations to allow accurate computation of the regression rate of the fuel.

42 ENGINEERING↗
DOE OSTI2021

Rapid 3D nanoscale coherent imaging via physics-aware deep learning

Phase retrieval, the problem of recovering lost phase information from measured intensity alone, is an inverse problem that is widely faced in various imaging modalities ranging from astronomy to nanoscale imaging. The current process of phase recovery is iterative in nature. As a result, the image formation is time consuming and computationally expensive, precluding real-time imaging. Here, we use 3D nanoscale X-ray imaging as a representative example to develop a deep learning model to address this phase retrieval problem. We introduce 3D-CDI-NN, a deep convolutional neural network and differential programing framework trained to predict 3D structure and strain, solely from input 3D X-ray coherent scattering data. Our networks are designed to be “physics-aware” in multiple aspects; in that the physics of the X-ray scattering process is explicitly enforced in the training of the network, and the training data are drawn from atomistic simulations that are representative of the physics of the material. We further refine the neural network prediction through a physics-based optimization procedure to enable maximum accuracy at lowest computational cost. 3D-CDI-NN can invert a 3D coherent diffraction pattern to real-space structure and strain hundreds of times faster than traditional iterative phase retrieval methods. Our integrated machine learning and differential programing solution to the phase retrieval problem is broadly applicable across inverse problems in other application areas.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2018

An instrument for in situ time-resolved X-ray imaging and diffraction of laser powder bed fusion additive manufacturing processes

In situ X-ray-based measurements of the laser powder bed fusion (LPBF) additive manufacturing process produce unique data for model validation and improved process understanding. Synchrotron X-ray imaging and diffraction provide high resolution, bulk sensitive information with sufficient sampling rates to probe melt pool dynamics as well as phase and microstructure evolution. Here, we describe a laboratory-scale LPBF test bed designed to accommodate diffraction and imaging experiments at a synchrotron X-ray source during LPBF operation. We also present experimental results using Ti-6Al-4V, a widely used aerospace alloy, as a model system. Both imaging and diffraction experiments were carried out at the Stanford Synchrotron Radiation Lightsource. Melt pool dynamics were imaged at frame rates up to 4 kHz with a ~1.1 μm effective pixel size and revealed the formation of keyhole pores along the melt track due to vapor recoil forces. Diffraction experiments at sampling rates of 1 kHz captured phase evolution and lattice contraction during the rapid cooling present in LPBF within a ~50 × 100 μm area. In conclusion, we also discuss the utility of these measurements for model validation and process improvement.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Characterization of damage processes in Montney siltstone under triaxial compression using acoustic emission and diagnostic imaging

SUMMARY Crack nucleation and rock failure processes in a fine-grained siltstone (Montney Formation) under triaxial compression are investigated using combined diagnostic techniques, including ultrasonic-wave measurement, acoustic-emission (AE) monitoring, computed tomography (CT) scanning, and thin-section imaging. The sample displays a weak-to-moderate inherent seismic anisotropy and noticeable stress-induced anisotropy prior to failure. No AE event was detected until the applied axial stress reached 95 per cent of the peak value. The signal-to-noise ratio is relatively low, however, and detectable AE events are more diffuse than those observed in highly brittle rocks. The AE locations correlate with a shear fracture zone imaged by CT scanning. AE moment-tensor analysis reveals that events with larger relative magnitudes are characterized by high volumetric (tensile or compressive) components, and the initiation of the failure zone is dominated by combined shear-tensile (ST) failure. Stress inversion of the AE events with high tensile components is in good agreement with the known applied stress. Microscopic imaging of thin sections from the failed sample shows that the failure zone is an en echelon structure consisting of a major fracture with branching micro and minor cracks. This failure mechanism is consistent with a ST source mechanism and is interpreted to be associated with the fine granular structure and mineral composition of Montney siltstone.

Jia, Suzie Q. (ORCID:0000000326496190)↗
DOE OSTI2017

Computational and compressive imaging via additively manufactured random optical element

Here, the ubiquity of computer processing power has enabled new imaging architectures that significantly depart from traditional imaging devices. Imaging systems with deeply enmeshed algorithmic elements are known as computational imaging systems, and can take unconventional physical forms compared to photographic systems. We report on the development and evaluation of a lensless imaging system utilizing an additively manufactured transparent optical element capable of producing image reconstructions after a calibration process. A generalized metric for image quality is presented and used to evaluate system performance as a function of total samples used to calibrate and test.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Real time observation of binder jetting printing process using high-speed X-ray imaging

A high-speed synchrotron X-ray imaging technique was used to investigate the binder jetting additive manufacturing (AM) process. A commercial binder jetting printer with droplet-on-demand ink-jet print-head was used to print single lines on powder beds. The printing process was recorded in real time using high-speed X-ray imaging. The ink-jet droplets showed distinct elongated shape with spherical head, long tail, and three to five trailing satellite droplets. Significant drift was observed between the impact points of main droplet and satellite droplets. The impact of the droplet on the powder bed caused movement and ejection of the powder particles. The depth of disturbance in the powder bed from movement and ejection was defined as interaction depth, which is found to be dependent on the size, shape, and material of the powder particles. For smaller powder particles (diameter less than 10 μm), three consecutive binder droplets were observed to coalesce to form large agglomerates. As a result, the observations reported here will facilitate the understanding of underlying physics that govern the binder jetting processes, which will then help in improving the quality of parts manufactured using this AM process.

42 ENGINEERING↗
DOE OSTI2016

TU-H-206-04: An Effective Homomorphic Unsharp Mask Filtering Method to Correct Intensity Inhomogeneity in Daily Treatment MR Images

Purpose: The daily treatment MRIs acquired on MR-IGRT systems, like diagnostic MRIs, suffer from intensity inhomogeneity issue, associated with B1 and B0 inhomogeneities. An improved homomorphic unsharp mask (HUM) filtering method, automatic and robust body segmentation, and imaging field-of-view (FOV) detection methods were developed to compute the multiplicative slow-varying correction field and correct the intensity inhomogeneity. The goal is to improve and normalize the voxel intensity so that the images could be processed more accurately by quantitative methods (e.g., segmentation and registration) that require consistent image voxel intensity values. Methods: HUM methods have been widely used for years. A body mask is required, otherwise the body surface in the corrected image would be incorrectly bright due to the sudden intensity transition at the body surface. In this study, we developed an improved HUM-based correction method that includes three main components: 1) Robust body segmentation on the normalized image gradient map, 2) Robust FOV detection (needed for body segmentation) using region growing and morphologic filters, and 3) An effective implementation of HUM using repeated Gaussian convolution. Results: The proposed method was successfully tested on patient images of common anatomical sites (H/N, lung, abdomen and pelvis). Initial qualitative comparisons showed that this improved HUM method outperformed three recently published algorithms (FCM, LEMS, MICO) in both computation speed (by 50+ times) and robustness (in intermediate to severe inhomogeneity situations). Currently implemented in MATLAB, it takes 20 to 25 seconds to process a 3D MRI volume. Conclusion: Compared to more sophisticated MRI inhomogeneity correction algorithms, the improved HUM method is simple and effective. The inhomogeneity correction, body mask, and FOV detection methods developed in this study would be useful as preprocessing tools for many MRI-related research and clinical applications in radiotherapy. Authors have received research grants from ViewRay and Varian.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Geometrical-Based Generative Adversarial Network to Enhance Digital Rock Image Quality

X-ray microcomputed tomography (micro-CT) is a common tool for the study of porous media structures and properties. High-quality micro-CT data are required to accurately capture pore structures. Acquiring high-quality micro-CT data, however, is not always possible, owing to application limitations and experimental constraints. Therefore, we propose a geometrical-based generative adversarial network (GAN) to rapidly restore noisy micro-CT images to their clean counterparts. The training data and related ground-truth (GT) data are scanned for 7 min and 9.5 h, respectively. To evaluate the performance of the geometrical-based GAN, a 6003 voxel image that has never been used for training is reconstructed and compared with the corresponding GT image. Histogram matching and linear normalization are implemented to adjust the histogram of the reconstructed image to that of the GT image. A watershed-based segmentation method is then applied to delineate pore and solid phases. Lastly, we measure the Minkowski functionals and petrophysical properties, including absolute permeability, pore size distribution, drainage capillary pressure-saturation curve, and imbibition relative permeability, to estimate the physical accuracy of the denoised image. The results show that the proposed geometrical-based GAN can accurately restore noisy micro-CT data. By reducing the scanning time from 9.5 h to 7 min, the expenditure of collecting micro-CT can be decreased significantly. This is particularly important for applications where time-lapse images of a dynamic process are required, high-throughput imaging is necessary for real-time data analysis, or where the quantification of large sample volumes is required.

58 GEOSCIENCES↗
DOE OSTI2019

Characterization of Exoplanet Atmospheres with the Optical Coronagraph on WFIRST

Wide-Field Infrared Survey Telescope (WFIRST)-CGI is a NASA technology demonstration mission that is charged with demonstrating key technologies for future exo-Earth imaging missions in space. In the process, it will obtain images and low-resolution spectra of a handful to a dozen extrasolar planets and possibly protoplanetary disks. Its unprecedented contrast levels in the optical will provide astronomers’ with their first direct look at mature, Jupiter-sized planets at moderate separations. This paper addresses the question: what science can be done with such data? An analytic noise model, which is informed by the ongoing engineering developments, is used to compute maximum achievable signal-to-noise ratios and scientifically viable integration times for hypothetical star–planet systems, as well as to investigate the constraining power of various combinations of WFIRST-CGI photometric and spectral observations. This work introduces two simple models for planetary geometric albedos, which are inspired largely by the solar system’s gas giants. The first planet model is a hybrid Jupiter–Neptune model, which separately treats the short and long wavelengths where chromophores and methane dominate absorption, respectively. The second planet model fixes cloud and haze properties in CoolTLusty to match Jupiter’s albedo spectrum, it then perturbs only the metallicity. MCMC retrievals performed on simulated observations are used to assess the precision with which planet model parameters can be measured subject to different exposure times and observing cases. Planet radius is recovered within ±15% for all observing cases with both the hybrid model and the CoolTLusty metallicity grid. Fit results for both models’ parameterizations of geometric albedo spectra demonstrate that a rough indication of the metallicity or methane content should be possible for some WFIRST-CGI targets. We conclude that real observations will likely be able to differentiate between extreme cases using these models, but will lack the precision necessary to uncover subtle trends.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

What’s the Difference? The Potential for Convolutional Neural Networks for Transient Detection without Template Subtraction

Abstract We present a study of the potential for convolutional neural networks (CNNs) to enable separation of astrophysical transients from image artifacts, a task known as “real–bogus” classification, without requiring a template-subtracted (or difference) image, which requires a computationally expensive process to generate, involving image matching on small spatial scales in large volumes of data. Using data from the Dark Energy Survey, we explore the use of CNNs to (1) automate the real–bogus classification and (2) reduce the computational costs of transient discovery. We compare the efficiency of two CNNs with similar architectures, one that uses “image triplets” (templates, search, and difference image) and one that takes as input the template and search only. We measure the decrease in efficiency associated with the loss of information in input, finding that the testing accuracy is reduced from ∼96% to ∼91.1%. We further investigate how the latter model learns the required information from the template and search by exploring the saliency maps. Our work (1) confirms that CNNs are excellent models for real–bogus classification that rely exclusively on the imaging data and require no feature engineering task and (2) demonstrates that high-accuracy (>90%) models can be built without the need to construct difference images, but some accuracy is lost. Because, once trained, neural networks can generate predictions at minimal computational costs, we argue that future implementations of this methodology could dramatically reduce the computational costs in the detection of transients in synoptic surveys like Rubin Observatory's Legacy Survey of Space and Time by bypassing the difference image analysis entirely.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Legacy Survey of Space and Time Data Preview 1: visit_image dataset type

The Legacy Survey of Space and Time Data Preview 1 (DP1) is the first release of data from the NSF-DOE Vera C. Rubin Observatory. It consists of raw and calibrated single-epoch images, co-adds, difference images, detection catalogs, and other derived data products. DP1 is based on 1792 science-grade optical/near-infrared exposures acquired over 48 distinct nights by the Rubin Commissioning Camera, LSSTComCam, on the Simonyi Survey Telescope at the Summit Facility on Cerro Pachón, Chile during the first on-sky commissioning campaign in late 2024. DP1 covers a total of approximately 15 sq. deg. over seven roughly equally-sized non-contiguous fields, each independently observed in six broad photometric bands, ugrizy, spanning a range of stellar densities and latitudes and overlapping with external reference datasets. This dataset is a subset of the full data release consisting of the visit_image dataset type. These are individual processed and calibrated sky images obtained from a single observation with a single filter. This release contains 15,972 datasets of this type.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

TU-FG-BRB-07: GPU-Based Prompt Gamma Ray Imaging From Boron Neutron Capture Therapy

Purpose: The purpose of this research is to perform the fast reconstruction of a prompt gamma ray image using a graphics processing unit (GPU) computation from boron neutron capture therapy (BNCT) simulations. Methods: To evaluate the accuracy of the reconstructed image, a phantom including four boron uptake regions (BURs) was used in the simulation. After the Monte Carlo simulation of the BNCT, the modified ordered subset expectation maximization reconstruction algorithm using the GPU computation was used to reconstruct the images with fewer projections. The computation times for image reconstruction were compared between the GPU and the central processing unit (CPU). Also, the accuracy of the reconstructed image was evaluated by a receiver operating characteristic (ROC) curve analysis. Results: The image reconstruction time using the GPU was 196 times faster than the conventional reconstruction time using the CPU. For the four BURs, the area under curve values from the ROC curve were 0.6726 (A-region), 0.6890 (B-region), 0.7384 (C-region), and 0.8009 (D-region). Conclusion: The tomographic image using the prompt gamma ray event from the BNCT simulation was acquired using the GPU computation in order to perform a fast reconstruction during treatment. The authors verified the feasibility of the prompt gamma ray reconstruction using the GPU computation for BNCT simulations.

60 APPLIED LIFE SCIENCES↗