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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Camera / Raw Data

The SUMR-D CART2 turbine data are recorded by the CART2 wind turbine's supervisory control and data acquisition (SCADA) system for the Advanced Research Projects Agency–Energy (ARPA-E) SUMR-D project located at the National Renewable Energy Laboratory (NREL) Flatirons Campus. For the project, the CART2 wind turbine was outfitted with a highly flexible rotor specifically designed and constructed for the project. More details about the project can be found here: https://sumrwind.com/. The data contain video data of the wind turbine blades during operation as well as while parked. Since the blades had a coning angle, the blades are only in frame of the video camera when the blades are in a pitched to run configuration.

17 WIND ENERGY↗
DOE OSTI2022

X-Ray Imaging And Assessment Of Non-Perturbing Magnetic Diagnostics For Intermediate-Density Fusion Experiments

This research adds to the understanding of when, why, where, and how fusion-grade plasmas generate X-rays. Normal fusion reactions create different spectra of X-rays from unwanted spurious situations so examination of the X-ray spectra provides information on whether a fusion device is operating properly or not. The characteristic time scale of the X-rays ranges from a few nanoseconds to a microseconds so a movie camera having a very high framing rate is required. The energy of the X-rays is not known and so the camera should provide information on what this energy is. The mechanism by which the X-rays are generated varies depend on whether the X-rays are from normal operation or from unwanted spurious effects. This project developed prototypes of a high-speed X-ray movie camera using two different technologies. These were tested on pulsed plasma experiments at Caltech. Despite having modest plasma parameters these experiments generated transient X-ray bursts suitable for developing and testing these cameras. One of the cameras was used on the MIFTI fusion-related experiment at UCSD and provided information on the energy, location, and timing of X-rays produced there. The camera could measure X-rays having energies from about 5 kilovolts to about 30 kilovolts with 40 nanosecond time resolution and space resolution of a few mm.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Implosion dynamics of triple-nozzle gas-puff z pinches on COBRA

Experiments on the 1-MA, 220-ns COBRA generator at Cornell University were conducted to provide detailed measurements of structured cylindrical gas-puff z pinches. In the experiments, a 7 cm diameter triple-nozzle gas valve assembly with concentric outer and inner annular nozzles and a central gas jet initialize the z-pinch load with various working gases, radial density profiles, and externally applied axial magnetic fields. Planar laser-induced fluorescence provides a measure of the initial neutral gas density of the load, while three-frame laser shearing interferometry and multi-frame extreme ultraviolet (XUV) cameras reveal the formation and propagation of a magneto-Rayleigh–Taylor (MRT) unstable shock layer. Implosion trajectories are compared to simple, experimentally informed models and found to be in good agreement. Differences in the structure of the accelerating plasma sheath and evolution of the MRT instability are observed for different gas species and axial magnetic field strengths, correlating with differences in pinch uniformity and x-ray emission. Here, the average instability growth is compared to linear MRT theory predictions using the instantaneous acceleration of the best-fit implosion models and characteristic instability wavelength, with the effective Atwood number and seed perturbation size as fit parameters. For high density argon center jets, ionization prior to the arrival of the imploding plasma sheath suggests a heating mechanism consistent with photoionization by XUV self-emission.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Computational microscopy for fast widefield deep-tissue fluorescence imaging using a commercial dual-cannula probe

A solid-glass cannula serves as a micro-endoscope that can deliver excitation light deep inside tissue while also collecting emitted fluorescence. Then, we utilize deep neural networks to reconstruct images from the collected intensity distributions. By using a commercially available dual-cannula probe, and training a separate deep neural network for each cannula, we effectively double the field of view compared to prior work. We demonstrated ex vivo imaging of fluorescent beads and brain slices and in vivo imaging from whole brains. We clearly resolved 4 µm beads, with FOV from each cannula of 0.2 mm (diameter), and produced images from a depth of ∼1.2 mm in the whole brain, currently limited primarily by the labeling. Since no scanning is required, fast widefield fluorescence imaging limited primarily by the brightness of the fluorophores, collection efficiency of our system, and the frame rate of the camera becomes possible.

42 ENGINEERING↗
DOE OSTI2021

Triton Field Trials Changes in Habitat 360-degree Underwater Videos

This dataset contains the underwater 360-degree video files recorded with a Boxfish 360 camera in La Jolla, CA, near a gravity base anchor of the CalWave xWave wave energy converter in December 2021 over three days, at dawn, noon, and dusk. It was generated to test the ability of using this type of camera mounted on an aluminum frame as a video lander to monitor the artificial reef effect of marine energy devices and associated seafloor structures. The Boxfish 360 is made of 3 cameras each recording its own set of videos. The videos are MOV files that can be viewed individually with any video reader but need to be stitched together to create the 360-degree footage. This dataset contains all the raw video files collected at dawn, noon and dusk on 11/30/2021, 12/01/2021 and 12/02/2021, for about 1h each time. This dataset is associated with the journal manuscript below (linked in resources): Hemery, L.G.; Mackereth, K.F.; Gunn, C.M.; Pablo, E.B. Use of a 360-Degree Underwater Camera to Characterize Artificial Reef and Fish Aggregating Effects around Marine Energy Devices. J. Mar. Sci. Eng. 2022, 10, 555. https://doi.org/10.3390/jmse10050555

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Radiation Imaging with Event Camera

Neuromorphic or event-based imaging is a new, commercially available sensor technology inspired by how the human eye works. Instead of measuring frames at a fixed rate, the camera measures changes in pixel intensity asynchronously. This difference in readout architecture results in a high dynamic range and low latency. Event-based cameras have been used in a variety of applications, including object tracking, navigation, and lidar technologies. However, event-based cameras have not been adequately researched for their ability to image high-energy particles. This report explores the use of an event camera for imaging alpha, beta, and X-rays particles, when coupled with scintillator screens to convert high-energy particles into visible light. Methods to process event data were developed and are presented here, along with the results. The event camera can measure alpha and beta particles with comparable performance to that of a conventional camera. Event cameras can also image higher-activity sources and offer the possibility of discriminating particle interaction types on the basis of timing differences, which typical cameras cannot do. Additionally, event cameras can image objects with an X-ray source when the source strength dynamically changes but does not create a high-contrast image during static X-ray measurement.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Dictionary Learning with Accumulator Neurons

The Locally Competitive Algorithm (LCA) uses local competition between non-spiking leaky integrator neurons to infer sparse representations, allowing for potentially real-time execution on massively parallel neuromorphic architectures such as Intel's Loihi processor. Here, we focus on the problem of inferring sparse representations from streaming video using dictionaries of spatiotemporal features optimized in an unsupervised manner for sparse reconstruction. Non-spiking LCA has previously been used to achieve unsupervised learning of spatiotemporal dictionaries composed of convolutional kernels from raw, unlabeled video. We demonstrate how unsupervised dictionary learning with spiking LCA (\hbox{S-LCA}) can be efficiently implemented using accumulator neurons, which combine a conventional leaky-integrate-and-fire (\hbox{LIF}) spike generator with an additional state variable that is used to minimize the difference between the integrated input and the spiking output. We demonstrate dictionary learning across a wide range of dynamical regimes, from graded to intermittent spiking, for inferring sparse representations of both static images drawn from the CIFAR database as well as video frames captured from a DVS camera. On a classification task that requires identification of the suite from a deck of cards being rapidly flipped through as viewed by a DVS camera, we find essentially no degradation in performance as the LCA model used to infer sparse spatiotemporal representations migrates from graded to spiking. We conclude that accumulator neurons are likely to provide a powerful enabling component of future neuromorphic hardware for implementing online unsupervised learning of spatiotemporal dictionaries optimized for sparse reconstruction of streaming video from event based DVS cameras.

artificial intelligence↗
DOE OSTI2022

Crystallization kinetics and thermodynamics of an Ag–In–Sb–Te phase change material using complementary in situ microscopic techniques

The crystallization of an amorphous Ag–In–Sb–Te (AIST) phase change material (PCM) is studied using multiple in situ imaging techniques to directly quantify crystal growth rates over a broad range of temperatures. The measurable growth rates span from ≈ 10 –9 to ≈ 20 m/s. Recent results using dynamic transmission electron microscopy (TEM), a photoemission TEM technique, and TEM with sub-framed imaging are reported here and placed into the context of previous growth rate measurements on AIST. Dynamic TEM experiments show a maximum observed crystal growth rate for as-deposited films to be > 20 m/s. It is shown that crystal growth above the glass transition can be imaged in a TEM through use of subframing and a high-frame-rate direct electron detection camera. Challenges associated with the determination of temperature during in situ TEM experiments are described. Finally, preliminary nanocalorimetry results demonstrate the feasibility of collecting thermodynamic data for crystallization of PCMs with simultaneous TEM imaging.

36 MATERIALS SCIENCE↗
DOE OSTI2020

MIRC-X: A Highly Sensitive Six-telescope Interferometric Imager at the CHARA Array

Michigan InfraRed Combiner-eXeter (MIRC-X) is a new highly sensitive six-telescope interferometric imager installed at the CHARA Array that provides an angular resolution equivalent of up to a 330 m diameter baseline telescope in J- and H-band wavelengths ((λ/(2B))∼0.6 mas). We upgraded the original Michigan InfraRed Combiner (MIRC) instrument to improve sensitivity and wavelength coverage in two phases. First, a revolutionary sub-electron noise and fast-frame-rate C-RED ONE camera based on an SAPHIRA detector was installed. Second, a new-generation beam combiner was designed and commissioned to (i) maximize sensitivity, (ii) extend the wavelength coverage to J band, and (iii) enable polarization observations. A low-latency and fast-frame-rate control software enables high-efficiency observations and fringe tracking for the forthcoming instruments of the CHARA Array. Since mid-2017, MIRC-X has been offered to the community and has demonstrated best-case H-band sensitivity down to 8.2 correlated magnitude. MIRC-X uses single-mode fibers to coherently combine the light from six telescopes simultaneously with an image-plane combination scheme and delivers a visibility precision better than 1%, and closure phase precision better than 1°. MIRC-X aims at (i) imaging protoplanetary disks, (ii) detecting exoplanets with precise astrometry, and (iii) imaging stellar surfaces and starspots at an unprecedented angular resolution in the near-infrared. In this paper, we present the instrument design, installation, operation, and on-sky results, and demonstrate the imaging capability of MIRC-X on the binary system ι Peg. The purpose of this paper is to provide a solid reference for studies based on MIRC-X data and to inspire future instruments in optical interferometry.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Real-Time Interactive 4D-STEM Phase-Contrast Imaging From Electron Event Representation Data: Less computation with the right representation

The arrival of direct electron detectors (DED) with high frame-rates in the field of scanning transmission electron microscopy has enabled many experimental techniques that require collection of a full diffraction pattern at each scan position, a field which is subsumed under the name four dimensional-scanning transmission electron microscopy (4D-STEM). DED frame rates approaching 100 kHz require data transmission rates and data storage capabilities that exceed commonly available computing infrastructure. Current commercial DEDs allow the user to make compromises in pixel bit depth, detector binning or windowing to reduce the per-frame file size and allow higher frame rates. This change in detector specifications requires decisions to be made before data acquisition that may reduce or lose information that could have been advantageous during data analysis. The 4D Camera, a DED with 87 kHz frame-rate developed at Lawrence Berkeley National Laboratory, reduces the raw data to a linear-index encoded electron event representation (EER). Here we show with experimental data from the 4D Camera that linear-index encoded EER and its direct use in 4D-STEM phase contrast imaging methods enables real-time, interactive phase-contrast from large-area 4D-STEM datasets. Furthermore, we detail the computational complexity advantages of the EER and the necessary computational steps to achieve real-time interactive ptychography and center-of-mass differential phase contrast using commonly available hardware accelerators.

4D-STEM↗
DOE OSTI2023

Real-Time Multi-Vehicle Multi-Camera Tracking with Graph-Based Tracklet Features

An essential application in intelligent transportation systems is multi-target multi-camera tracking (MTMCT), where the target’s activity is tracked from different cameras. Although the tracking-by-detection scheme is the primary paradigm in MTMCT, the object association information from the video frames is lost. This is mainly because the multi-camera multi-object matching uses the information from the video frames separately. To solve this problem and leverage this association information, we propose an MTMCT framework, where features are built in the form of a graph and a graph similarity algorithm is used to match multi-camera objects. In this paper, we focus on the real-time scenario, where only the past images are used to match an object. Our method achieves an IDF1 score (the ratio of the number of correctly identified objects to the number of ground truth and average objects) of 0.75 with a rate of 14 frames per second (fps).

Engineering↗
DOE OSTI2022

Digital coded exposure formation of frames from event-based imagery

Abstract Event-driven neuromorphic imagers have a number of attractive properties including low-power consumption, high dynamic range, the ability to detect fast events, low memory consumption and low band-width requirements. One of the biggest challenges with using event-driven imagery is that the field of event data processing is still embryonic. In contrast, decades worth of effort have been invested in the analysis of frame-based imagery. Hybrid approaches for applying established frame-based analysis techniques to event-driven imagery have been studied since event-driven imagers came into existence. However, the process for forming frames from event-driven imagery has not been studied in detail. This work presents a principled digital coded exposure approach for forming frames from event-driven imagery that is inspired by the physics exploited in a conventional camera featuring a shutter. The technique described in this work provides a fundamental tool for understanding the temporal information content that contributes to the formation of a frame from event-driven imagery data. Event-driven imagery allows for the application of arbitrary virtual digital shutter functions to form the final frame on a pixel-by-pixel basis. The proposed approach allows for the careful control of the spatio-temporal information that is captured in the frame. Furthermore, unlike a conventional physical camera, event-driven imagery can be formed into any variety of possible frames in post-processing after the data is captured. Furthermore, unlike a conventional physical camera, coded-exposure virtual shutter functions can assume arbitrary values including positive, negative, real, and complex values. The coded exposure approach also enables the ability to perform applications of industrial interest such as digital stroboscopy without any additional hardware. The ability to form frames from event-driven imagery in a principled manner opens up new possibilities in the ability to use conventional frame-based image processing techniques on event-driven imagery.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Individual Wave Detection and Tracking within a Rotating Detonation Engine through Computer Vision Object Detection applied to High-Speed Images

Known for their simplistic design and continuous detonation, rotating detonation engines (RDEs) constitute a majority of current pressure gain combustion (PGC) research efforts. Experimental RDE operation times have been continuously extended through the use of rig cooling techniques. As the window of observable behavior is expanded, and as the technology matures toward eventual integration within gas turbines, monitoring techniques must evolve to better match industrial diagnostics. High-speed image analysis techniques prove useful to capture and evaluate the unsteady detonation behavior within the RDE. Traditional image analysis techniques, however, require extensive processing times which prohibit simultaneous monitoring. To better address this problem, a computer vision object detection methodology is proposed to quickly detect individual detonation waves within a single down-axis image. Detonation waves are detected in individual images by the implemented computer vision method You Only Look Once (YOLO) object detection network. In order to detect detonation waves, the network must first be trained using RDE images of interest, for which each required phase of network development is outlined. Detection of waves is improved through proper treatment of the collected image set, variation of Intersection over Union (IoU) and confidence thresholding, and through a parametric study of annotation dimensions. Each detected wave is described by its location and rotational direction, and locations are tracked to calculate wave velocity across each frame, leading to a timestep resolution of 20 µs. Wave velocities are also calculated through a series of frames, leading to a suitable average velocity estimation using as few as 10 frames. Uncertainty analysis accounting for variation in camera framerate, pixel width and annotation centroid locations estimates a total uncertainty of ±4.3% for velocity calculations, using the smallest annotation boxes. This method offers great reductions in processing times, as a step toward real-time monitoring of detonation waves within an RDE. Improving on previous studies, this technique is impartial to wave modes not included in the original training set and calculates wave velocities independent of high-speed pressure data. The ability to isolate waves within predicted bounding boxes will likely facilitate analysis of pixel intensity variation as an estimation of wave strength in future work.

Johnson, Kristyn↗
DOE OSTI2021

Individual Wave Detection and Tracking within a Rotating Detonation Engine through Computer Vision Object Detection applied to High-Speed Images

Known for their simplistic design and continuous detonation, rotating detonation engines (RDEs) constitute a majority of current pressure gain combustion (PGC) research efforts. Experimental RDE operation times have been continuously extended through the use of rig cooling techniques. As the window of observable behavior is expanded, and as the technology matures toward eventual integration within gas turbines, monitoring techniques must evolve to better match industrial diagnostics. High-speed image analysis techniques prove useful to capture and evaluate the unsteady detonation behavior within the RDE. Traditional image analysis techniques, however, require extensive processing times which prohibit simultaneous monitoring. To better address this problem, a computer vision object detection methodology is proposed to quickly detect individual detonation waves within a single down-axis image. Detonation waves are detected in individual images by the implemented computer vision method You Only Look Once (YOLO) object detection network. In order to detect detonation waves, the network must first be trained using RDE images of interest, for which each required phase of network development is outlined. Detection of waves is improved through proper treatment of the collected image set, variation of Intersection over Union (IoU) and confidence thresholding, and through a parametric study of annotation dimensions. Each detected wave is described by its location and rotational direction, and locations are tracked to calculate wave velocity across each frame, leading to a timestep resolution of 20 µs. Wave velocities are also calculated through a series of frames, leading to a suitable average velocity estimation using as few as 10 frames. Uncertainty analysis accounting for variation in camera framerate, pixel width and annotation centroid locations estimates a total uncertainty of ±4.3% for velocity calculations, using the smallest annotation boxes. This method offers great reductions in processing times, as a step toward real-time monitoring of detonation waves within an RDE. Improving on previous studies, this technique is impartial to wave modes not included in the original training set and calculates wave velocities independent of high-speed pressure data. The ability to isolate waves within predicted bounding boxes will likely facilitate analysis of pixel intensity variation as an estimation of wave strength in future work.

Johnson, Kristyn↗
DOE OSTI2022

Blast Effects on Buildings (Final Report)

Lawrence Livermore National Laboratory (LLNL) has conservatively reduced the explosive safety standards suggested by the Small Quantities in Research Laboratories (SQRL) program testing by 60%. For this reason, further research into the detailed effects of small amounts of explosives in typically constructed rooms is needed in order to improve their factor of safety. The team was tasked with designing an experiment to investigate the effect of different variables on drywall under explosive blasts. In order to meet this objective, the team conducted a comprehensive literature review to gain an understanding of industry-standard construction practices and review previous tests conducted by the Army corp of engineers. Since the team used PBXN-5 rather than C4, as in SQRL, an initial shot was conducted to compare damage levels. From those results and the physical constraints of the testing chamber, the team redesigned multiple single-panel drywall frames to capture the entirety of the incurred damage. Proposed designs were narrowed down using a decision matrix. From the study of previous tests and literature review, variables were chosen that the team hypothesized to have an impact on drywall strength. The variables that were tested were chosen from the results of that work and specific variables the sponsor was interested in, and they were paint, humidity/moisture content, and explosive positioning relative to the studs. Detailed plans were made for each variable according to what conditions the team wanted to investigate. For humidity, this involved testing low, ambient, and high conditions by treating the panels in a chamber. Preliminary shots were performed to test the structural integrity of the frame and streamline the test diagnostics which involved a high-speed camera placed behind the drywall, outside of the chamber, and a pressure probe placed behind the drywall, inside the chamber. Once the instrumentation, diagnostics, and frame design were finalized, a quantitative damage criteria matrix was created to categorize the results of the main shot series. In conjunction with evidence from the high-speed video, the achieved damage levels indicate that high moisture content drywall is better able to withstand explosive blasts. Larger stud damage and lower drywall damage occurred when the explosive was located directly in front of a stud. Paint had no noticeable impact on strength. Ultimately, the team conducted a total of 17 tests, leaving the door open for future in-depth research into the impact of humidity.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Oak Ridge National Laboratory Evaluation of Stream-Trained Models in Practice

The goal of this integration is to replicate the results from the original paper Autonomous Utility Pole Identification on different camera hardware and integrate the model into a live video stream provided by the unmanned aerial system (UAS) itself while in operation. This involves retraining the original model and validating its efficacy on multiple camera modules to select the most effective device for installation. Moreover, this integration requires writing software to handle the reception of a real-time streaming protocol stream from the UAS and run each frame through the model while allowing a user to monitor the camera feed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

DETECTING FIRE WITH MACHINE LEARNING-ENABLED VISUAL MONITORING FOR NUCLEAR POWER PLANT ENVIRONMENTS

Nuclear power plants are experiencing significant cost challenges to remain competitive with other energy-generation utilities. Unlike other industries, the cost of operation and maintenance activities is mostly attributed to workforce costs. To mitigate this, nuclear power plant stakeholders are increasingly interested in the development and deployment of machine learning methods to potentially automate or augment manually intensive tasks to reduce costs, especially for monitoring activities. One monitoring function that is visually demanding and that can occur frequently to meet the requirements of a fire protection program is visually monitoring an area for fire occurrence. Currently, fire watch activities consist of a worker physically stationed at a given location with the sole responsibility of observing a given area to ensure a fire is detected and mitigated promptly. This effort focused on the development and evaluation of a suitable deep convolutional neural network to classify individual video frames at a sub-second frequency for the occurrence of “fire” and “no fire” in varying industrial environments similar to nuclear power plants. It is believed that a trained neural network model could be integrated with existing facility video surveillance camera feeds to generate alerts when fire inferences occur in individual frames captured at sub-second temporal resolutions. Extensive effort was dedicated to identifying and curating suitable imagery training data representing varying environments and scene settings with and without flame features to maximize generalization in nuclear power plant environments. The data collection effort resulted in the aggregation of a large, labeled image library exceeding 12,000 images to support model training for diverse industrial environments. A deep neural network model incorporating parallel multi-scale capabilities was developed and trained to support accurate image-based detection of flame incidents of varying sizes and spectral feature properties within heterogeneous scenes. Analysis results show that the trained model can achieve high inference accuracy despite heterogeneous scene environments and components. Testing accuracy exceeded 95.0 percent with very low false positive and false negative inferences.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗