Interpretation of defects in field ion images - Fcc materials.
Computer shell model simulating dislocation configurations selection in field ion images of fcc structures
Hren, J. J.↗
SEARCH · Search NASA
Results for “image simulation”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
CME Flux Rope and Shock Identifications and Locations: Comparison of White Light Data, Graduated Cylindrical Shell Model, and MHD Simulations
Coronal mass ejections (CMEs) are major transient phenomena in the solar corona that are observed with ground-based and spacecraft-based coronagraphs in white light or with in situ measurements by spacecraft. CMEs transport mass and momentum and often drive shocks. In order to derive the CME and shock trajectories with high precision, we apply the graduated cylindrical shell (GCS) model to fit a flux rope to the CME directed toward STEREO A after about 19:00 UT on 29 November 2013 and check the quality of the heliocentric distance-time evaluations by carrying out a three-dimensional magnetohydrodynamic (MHD) simulation of the same CME with the Block Adaptive Tree Solar-Wind Roe Upwind Scheme (BATS-R-US) code. Heliocentric distances of the CME and shock leading edges are determined from the simulated white light images and magnetic field strength data. We find very good agreement between the predicted and observed heliocentric distances, showing that the GCS model and the BATS-R-US simulation approach work very well and are consistent. In order to assess the validity of CME and shock identification criteria in coronagraph images, we also compute synthetic white light images of the CME and shock. We find that the outer edge of a cloud-like illuminated area in the observed and predicted images in fact coincides with the leading edge of the CME flux rope and that the outer edge of a faint illuminated band in front of the CME leading edge coincides with the CME-driven shock front.
Schmidt, J. M.↗
Classification of computed thermal tomography images with deep learning convolutional neural network
Thermal tomography (TT) is a computational method for the reconstruction of depth profile of the internal material defects from Pulsed Infrared Thermography (PIT) nondestructive evaluation. Here, the PIT method consists of recording material surface temperature transients with a fast frame infrared camera, following thermal pulse deposition on the material surface with a flashlamp and heat diffusion into material bulk. TT algorithm obtains depth reconstructions of thermal effusivity, which has been shown to provide visualization of the subsurface internal defects in metals. In many applications, one needs to determine the defect shape and orientation from reconstructed effusivity images. Interpretation of TT images is non-trivial because of blurring, which increases with depth due to the heat diffusion-based nature of image formation. We have developed a deep learning convolutional neural network (CNN) to classify the size and orientation of subsurface material defects in TT images. CNN was trained with TT images produced with computer simulations of 2D metallic structures (thin plates) containing elliptical subsurface voids. The performance of CNN was investigated using test TT images developed with computer simulations of plates containing elliptical defects, and defects with shapes imported from scanning electron microscopy images. CNN demonstrated the ability to classify radii and angular orientation of elliptical defects in previously unseen test TT images. We have also demonstrated that CNN trained on the TT images of elliptical defects is capable of classifying the shape and orientation of irregular defects.
42 ENGINEERING↗
Ocean Energy Sandia TEAMER WEC Simulation Results
Computational fluid simulations for wave energy converters and supporting materials from Ocean Energy's WEC Buoy TEAMER project in collaboration with Sandia National Laboratories. Each file includes images and video of simulation along with the simulation files for the case. The videos and images include air and water simulations of the buoy in 2D and 3D views. Data spreadsheets of the simulation outputs are also included in the files. The data are represented as different cases in which the wave height(Hs) and peak period(Tp) were different.
16 TIDAL AND WAVE POWER↗
Automatic Imagery Data Analysis for Proactive Computer-Based Workflow Management during Nuclear Power Plant Outages
This report is being submitted for the task “Final Report” of DOE NEUP Project 15-8121 “Automatic Imagery Data Analysis for Proactive Computer-Based Workflow Management during Nuclear Power Plant Outages.” Typical nuclear power plant (NPP) outages always involve thousands of maintenance and refueling activities and a large number of workers in limited workspaces, while having tight schedules and zero-tolerance for accidents. During an outage, thousands of workers will be working in various workspaces across the NPP. High outage costs and expensive delays (approximately 1.5 million dollars of loss per day of delay) in NPP maintenance demand tight outage schedules. In packed workspaces, an automatic system that monitors human behaviors in real-time and provides insights about current and pending schedule deviations from the plan is critical for ensuring: 1) effective collaboration among workers and worker teams from different trades; 2) less waste of time and resources due to the lack of situational awareness; and 3) proactive outage project control. The overall goal of this project is to test the hypothesis that real-time imagery-based object tracking and spatial analysis, as well as human behavior modeling of outage participants, will significantly improve the efficiency of outage control while lowering the rates of accidents and incidents. Three objectives of this project are: 1) Establish real-time object tracking and spatiotemporal analysis methods that automatically assess the productivity of field activities and detect anomalous spatiotemporal relationships among activities that cause inefficiencies and risks; 2) Establish realtime human tracking and human factor modeling methods for automatically diagnosing unexpected actions of and interactions between outage participants, those which cause inefficient collaborations between Advanced Outage Control Center (AOCC), satellite outage centers, NPP workers, and maintenance service providers; and 3) Test the proposed automated object tracking, human behavior modeling, and spatiotemporal analysis methods in outage control case studies in order to characterize the effectiveness of automated imagery-data-driven methods in proactively improving the efficiency and safety of workflows in outage coordination and risk management. Recent studies of detailed human behavior monitoring on construction sites have examined the potential of applying advanced computer vision algorithms in detecting and tracking anomalous workers (i.e., workers who do not wear hard hats or safety vests) for ensuring job site safety. Some studies of human factor studies revealed the importance of modeling detailed interactions between individuals within and across teams in better understanding the impact of human in proactive project control. Other studies in the construction domain have developed computational simulation frameworks that formalize detailed spatiotemporal interactions between tasks in order to simulate the impacts of individual tasks on the performance of workflows. While integrated analyses that combine human factor assessment, as well as image processing and simulation, are in demand for effective decision-making, limited studies have examined the potential of such integrated analyses in NPP outage control. This research project has examined an automatic outage monitoring and control system that integrates human factor analysis, computer vision techniques, and simulation methods in order to enable engineers to better understand the interactions between humans, resources, and workflows during outage processes. The project aims at providing NPP maintenance agencies insights into more efficient use of limited resources in extending the life of a nuclear plant, as well as reducing waste while ensuring sufficient generation of electricity. This study is significant for the safety of nuclear plants, sustainable electricity generation for livable communities, and cost savings for maintaining electricity infrastructures in the United States.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Chromatically corrected virtual image visual display
An in-line, three element, large diameter, optical display lens is disclosed which has a front convex-convex element, a central convex-concave element, and a rear convex-convex element. The lens, used in flight simulators, magnifies an image presented on a television monitor and, by causing light rays leaving the lens to be in essentially parallel paths, reduces eye strain of the simulator operator.
Kahlbaum, W. M., Jr.↗
Engineering workstation: Sensor modeling
The purpose of the engineering workstation is to provide an environment for rapid prototyping and evaluation of fusion and image processing algorithms. Ideally, the algorithms are designed to optimize the extraction of information that is useful to a pilot for all phases of flight operations. Successful design of effective fusion algorithms depends on the ability to characterize both the information available from the sensors and the information useful to a pilot. The workstation is comprised of subsystems for simulation of sensor-generated images, image processing, image enhancement, and fusion algorithms. As such, the workstation can be used to implement and evaluate both short-term solutions and long-term solutions. The short-term solutions are being developed to enhance a pilot's situational awareness by providing information in addition to his direct vision. The long term solutions are aimed at the development of complete synthetic vision systems. One of the important functions of the engineering workstation is to simulate the images that would be generated by the sensors. The simulation system is designed to use the graphics modeling and rendering capabilities of various workstations manufactured by Silicon Graphics Inc. The workstation simulates various aspects of the sensor-generated images arising from phenomenology of the sensors. In addition, the workstation can be used to simulate a variety of impairments due to mechanical limitations of the sensor placement and due to the motion of the airplane. Although the simulation is currently not performed in real-time, sequences of individual frames can be processed, stored, and recorded in a video format. In that way, it is possible to examine the appearance of different dynamic sensor-generated and fused images.
Pavel, M↗
SAMSI: An orbiting spatial interferometer for micro-arc second astronomical observations
The concept and performance of (SAMSI) Spacecraft Array for Michelson Spatial Interferometry, an orbiting spatial interferometer comprised of three free-flying spacecraft, two collector telescopes and a central mixing station are described. In the one-dimensional interferometry mode orbits exist which provide natural scanning of the baseline. These orbits place extremely small demands on thrusters and fuel consumption. Resolution of 0.00001 arcsecond and magnitude limits of mv = 15 to 20 are achievable in a single orbit. In the imaging mode, SAMSI could synthesize images equivalent to those produced by equal diameter filled apertures in space, making use of the fuel resupply capability of a space station. Simulations indicate that image reconstruction can be performed with milliarcsecond resolution to a visual magnitude 12 in 12 hr of spiral scanning integration time.
Stachnik, R. V.↗
Test methods for image-based information in next-generation manufacturing
Typical control designs in the process systems engineering literature have assumed that the primary sensing methodologies are traditional instruments such as thermocouples. Digitalization is changing the landscape for manufacturing, and data-based sensing modalities (e.g., image-based sensing) are becoming of greater interest for plant control. These considerations require novel test/evaluation solutions. For example, process systems engineering researchers may wish to test image-based sensors in simulation. In this work, we provide preliminary thoughts on how image-based technologies might be evaluated via simulation for process systems.
Henrique Oyama↗
Uncertainty quantification of the convolutional neural networks on permeability estimation from micro-CT scanned sandstone and carbonate rock images
Rock permeability is one of the most crucial properties affecting subsurface fluid flow behaviors. To accurately and robustly estimate the permeability, Digital Rock Physics, including micro-CT scanning technology and direct flow simulations on scanned images, has prevailed in recent years. Besides, machine learning techniques such as convolutional neural networks (CNNs) have been widely adopted and achieved success in permeability estimations directly from rock images. However, existing ML methods used for permeability estimation from rock images lack uncertainty quantification that causes unreliable predictions and overconfident estimations on out-of-distribution (OOD) samples. Here, in this work, we propose a PI3NN-CNN framework to address this problem. PI3NN-CNN consists of a CNN model for absolute permeability estimation and a PI3NN method to quantify the estimation uncertainty. It is able to quantify the uncertainty for in-distribution (InD) data with a desired confidence level, and identify OOD samples to avoid overconfident predictions. We demonstrate the method using micro-CT scanned images from two sandstone and two carbonate rocks. We found that PI3NN-CNN generates accurate predictions for InD samples, while producing high-quality prediction uncertainties regardless of the prediction accuracy. Meanwhile, PI3NN-CNN identifies OOD samples using its special network initialization scheme. The unique feature of PI3NN-CNN makes it applicable to more complex real-world image-based data for robust learning and predictions without overconfident estimations when the ground-truth information is unavailable.
58 GEOSCIENCES↗
Heliostat optical error inspection with polarimetric imaging drone
On a Concentrated Solar Power (CSP) field, optical errors have significant impacts on the collection efficiency of heliostats. Fast, cost-effective, labor-efficient, and non-intrusive autonomous field inspection remains a challenge. Approaches using imaging drone, i.e., Unmanned Aerial Vehicle (UAV) system integrated with high resolution visible imaging sensors, have been developed to address these challenges; however, these approaches are often limited by insufficient imaging contrast. Here, in this study, we report a polarimetry-based method with a polarization imaging system integrated on UAV to enhance imaging contrast for in-situ detection of heliostat mirrors without interrupting field operation. We developed an optical model for skylight polarization pattern to simulate the polarization images of heliostat mirrors and obtained optimized waypoints for polarimetric imaging drone flight path to capture images with enhanced contrast. The polarimetric imaging-based method improved the success rate of edge detections in scenarios which were challenging for mirror edge detection with conventional imaging sensors. We have performed field tests to achieve significantly enhanced heliostat edge detection success rate and investigate the feasibility of integrating polarimetric imaging method with existing imaging-based heliostat inspection methods, i.e., Polarimetric Imaging Heliostat Inspection Method (PIHIM). Our preliminary field test results suggest that the PIHIM hold the promise to enable sufficient imaging contrast for real-time autonomous imaging and detection of heliostat field, thus suitable for non-interruptive fast CSP field inspection during its operation.
CSP Field↗
Control Algorithms for Multi-Wavefront Sensor Adaptive Optics for Astronomical Exoplanet Imaging
This package consists of simulations of control algorithms being proposed for upcoming adaptive optics projects for exoplanet direct imaging, in which one wavefront affector (deformable mirror) is controlled by two wavefront sensors. Simulations include time-domain behavior under various assumed conditions, parameter optimization routines for the controllers, and stability analysis.
Sengupta, Aditya↗
Dust Survival in Galactic Winds
This repository contains three-dimensional volumetric data from an Eulerian hydrodynamical simulation (conducted on a uniform Cartesian grid) generated by the Cholla hydrodynamics code. The datasets contain snapshots (full-grid, projections, and slices) in the HDF5 format of a multi-phase medium in which a hot, diffuse, dust-free background wind accelerates a cool, dense cloud of gas and dust. This scenario is intended to represent a supernova-driven galactic outflow, in which hot supernova winds are thought to accelerate cool interstellar medium material out of the galactic disk into the surrounding circumgalactic medium. There are three separate datasets for simulations corresponding to three cloud evolutionary scenarios: long-term cloud survival (surv), marginal cloud survival (disr), and cloud destruction (dest). Projection and slice images of the simulations are also included in this repository.
79 ASTRONOMY AND ASTROPHYSICS↗
Evaluation of intracoronary hemodynamics identifies perturbations in vorticity
Coronary artery disease (CAD) is highly prevalent and associated with adverse events. Challenges have emerged in the treatment of intermediate coronary artery stenoses. These lesions are often interrogated with fractional flow reserve (FFR) testing to determine if a stenosis is likely to be causative for ischemia in a cardiac territory. This invasive test requires insertion of a pressure wire into a coronary vessel. Recently computational fluid dynamics (CFD) has been used to noninvasively assess fractional flow reserve in vessels reconstructed from medical imaging data. However, many of these simulations are unable to provide additional information about intravascular hemodynamics, including velocity, endothelial shear stress (ESS), and vorticity. We hypothesized that vorticity, which has demonstrated utility in the assessment of ventricular and aortic diseases, would also be an important hemodynamic factor in CAD. Three-dimensional (3D), patient-specific coronary artery geometries that included all vessels >1 mm in diameter were created from angiography data obtained from 10 patients who underwent diagnostic angiography and FFR testing (n = 9). A massively parallel CFD solver (HARVEY) was used to calculate coronary hemodynamic parameters including pressure, velocity, ESS, and vorticity. These simulations were validated by comparing velocity flow fields from simulation to both velocities derived from in vitro particle image velocimetry and to invasively acquired pressure wire-based data from clinical testing. There was strong agreement between findings from CFD simulations and particle image velocimetry experimental testing (p < 0.01). CFD-FFR was also highly correlated with invasively measured FFR (ρ = 0.77, p = 0.01) with an average error of 5.9 ± 0.1%. CFD-FFR also had a strong inverse correlation with the vorticity (ρ = -0.86, p = 0.001). Simulations to determine the effect of the coronary stenosis on intravascular hemodynamics demonstrated significant differences in velocity and vorticity (both p < 0.05). Further evaluation of an angiographically normal appearing non-FFR coronary vessel in patients with CAD also demonstrated differences in vorticity when compared with FFR vessels (p < 0.05). The use of highly accurate 3D CFD-derived intravascular hemodynamics provides additional information beyond pressure measurements that can be used to calculate FFR. Vorticity is one parameter that is modified by a coronary stenosis and appears to be abnormal in angiographically normal vessels in patients with CAD, highlighting a possible use-case in preventative screening for early coronary disease.
59 BASIC BIOLOGICAL SCIENCES↗
SI IV Resonance Line Emission during Solar Flares: Non-LTE, Non-equilibrium, Radiation Transfer Simulations
The Interface Region Imaging Spectrograph routinely observes the Si IV resonance lines. When analyzing quiescent observations of these lines, it has typically been assumed that they form under optically thin conditions. This is likely valid for the quiescent Sun, but this assumption has also been applied to the more extreme flaring scenario. We used 36 electron-beam-driven radiation hydrodynamic solar flare simulations, computed using the RADYN code, to probe the validity of this assumption. Using these simulated atmospheres, we solved the radiation transfer equations to obtain the non-LTE, nonequilibrium populations, line profiles, and opacities for a model silicon atom, including charge exchange processes. This was achieved using the "minority species" version of RADYN. The inclusion of charge exchange resulted in a substantial fraction of Si IV at cooler temperatures than those predicted by ionization equilibrium. All simulations with an injected energy flux F> 5 x 10(exp 10) erg cm(exp -2) s(exp -1) resulted in optical depth effects on the Si IV emission, with differences in both intensity and line shape compared to the optically thin calculation. Weaker flares (down to F ~ 10(exp 9) erg cm(exp -2) s(exp -1)) also resulted in Si IV emission forming under optically thick conditions, depending on the other beam parameters. When opacity was significant, the atmospheres generally had column masses in excess of 5 x 10-6 g cm-2 over the temperature range 40-100 kK, and the Si IV formation temperatures were between 30 and 60 kK. We urge caution when analyzing Si IV flare observations, or when computing synthetic emission without performing a full radiation transfer calculation.
Kerr, Graham S.↗
Simulation of the Effects of Hard Limiting on Image Quality of Synthetic Aperture Radar
Starting with a magnetic tape of a scene viewed by the Landsat satellite, the radar return of reflectors whose average intensity matched that of the picture elements in the scene has been simulated. The returns were processed in three ways: normally or with no quantization, with a procedure simulation IF hard limiting, and with a procedure simulating video (baseband) hard limiting. For each type of processing an image for a one, two, and four-look system has been developed. It was found that IF limiting is slightly better than video limiting, while both can be reasonable trade-offs of image quality for reduced data rates when the number of looks is four or less. These conclusions are supported by photographs representing the different processing techniques.
Lipes, R. G.↗
Cone Beam Computed Tomography: The Challenges and Strategies in Its Application for Dose Accumulation
Online image guidance using cone beam computed tomography (CBCT) has greatly improved the geometric precision of radiotherapy. Changes in anatomy are common during a course of fractionated treatment, resulting in dose deviation from the planned distribution. There is increased interest in performing dose accumulation to compute the actual delivered dose and to adapt the treatment when necessary. This can be achieved by delineating the volume of interest and by generating “dose of the day” through dose computation on the CBCT. However, the image quality and the accuracy of the CT number of CBCT are deemed to be inferior to fan beam CT, which increases the uncertainty associated in this process. A review of literature was conducted to assess the reliability of and to examine strategies for overcoming the challenges in using CBCT for volume delineation and dose computation. The review demonstrates that the uncertainty varies across body sites, and different strategies have been recommended to generate comparable results to images from CT simulators. This facilitates a better understanding of the potential and the limitation of using CBCT for dose accumulation.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
Three-Dimensional Flow Behavior Inside the Submerged Entry Nozzle
According to various authors, the surface quality of steel depends on the dynamic conditions that occur within the continuous casting mold’s upper region. The meniscus, found in that upper region, is where the solidification process begins. The liquid steel is distributed into the mold through a submerged entry nozzle (SEN). In this paper, the dynamic behavior inside the SEN is analyzed by means of physical experiments and numerical simulations. The particle imaging velocimetry technique was used to obtain the vector field in different planes and three-dimensional flow patterns inside the SEN volume. Moreover, large eddy simulation was performed, and the turbulence model results were used to understand the nonlinear flow pattern inside the SEN. Using scaled physical and numerical models, quasi-periodic behavior was observed due to the interaction of two three-dimensional vortices that move inside the SEN lower region located between the exit ports of the nozzle.
36 MATERIALS SCIENCE↗