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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

n-Propylbenzene Droplet Combustion: Experiments and Numerical Modeling

n-Propylbenzene (PB, C9H12, 432K boiling point) is important as a constituent in surrogates for some transportation fuels but little is understood about its droplet burning characteristics. We present an experimental and numerical study of a fuel droplet burning under conditions where the sole means of gas transport is fuel evaporation at the droplet surface thereby creating a one-dimensional gas transport that is amenable to detailed numerical modeling. The one-dimensional gas transport that results is amenable to detailed numerical modeling. Initial droplet diameters (Do) ranged from 0.3 mm to about 5 mm to better understand the mechanisms that control burning over such a wide range of Do. For Do < 1 mm droplets were burned while anchored to 14 m SiC fibers within a sealed container in free-fall during which time the droplets were ignited by spark discharge and the burning process recorded by video cameras. Larger and free-floating droplets were studied in the spaced-based platform of the orbiting International Space Station for Do > 1.0 mm which provided unlimited times for experimental observation. Measurements of droplet, flame and soot shell diameters were made and are discussed in the presentation. The experimental results showed formation of optically thick soot clouds developing during burning that required a robust image analysis algorithm to enable measurement of droplet diameter. The measured droplet diameters were well predicted by a numerical model that included soot formation, a PB kinetic mechanism comprising 576 species and 27369 reactions, unsteady gas and liquid transport, variable properties and radiative transport. Flame diameters showed more variability compared to the simulations, depending on how the flame was defined (peak gas temperature and OH concentration). Droplets burned slower as Do was increased and the PB sooting propensity increased with Do. Flame extinction was observed for Do > 3.3 mm indicating a strong effect of radiation as Do was increased compared to the smaller droplet diameters examined where extinction was not experimentally observed. For the largest droplets examined the possibility that the observed extinction is indicative of a transition to cool flame burning is still under investigation.

Guo, Songtao↗
DOE OSTI2022

Microcam: A Low Power and Privacy Preserving Multi-modal Platform for Occupancy Detection (Final Report)

Heating, ventilation, and air conditioning (HVAC) consumes a significant portion of the energy used in buildings. Much of this is wasted energy, used when buildings are either not occupied at all, or occupied well under their maximum design conditions. This project has focused on residential occupancy detection to autonomously control HVAC systems and save energy. Limitations of existing occupancy sensors include one or more of the following: (i) they employ sensors or algorithms that are not able to detect stationary occupants; (ii) they cannot classify the source of the motion (such as a pet); (iii) depending on the camera resolution and employed algorithms, they do not allow for embedded or onboard computation, and require external or cloud-based processing; (iv) many algorithms developed for camera-based systems are sensitive to lighting changes, and thus prone to missed detections or false alarms; (v) Most existing systems depend on adjustment of settings for different scenarios, complicating self-commissioning; (vi) they cannot provide high enough accuracy; (vii) they are costly; (viii) they are not battery-powered, thus limiting ease of use and installation. In this project, Syracuse University and its partner SRI have developed a low-cost, high accuracy, standalone residential occupancy sensing platform, referred to as the MicroCam, to address all of the aforementioned challenges. MicroCam can operate on typical alkaline batteries without relying on the “cloud” or external computing resources, and consists of low-power, Artificial Intelligence (AI)-based, IoT platforms. Each platform has multi-modal sensors and can process motion, audio and video data, and send binary occupancy result to a lead platform. All sensor data is processed locally on platforms, and the only transmitted data is the binary occupancy state. In addition, preliminary work has been done on images wherein occupants are not discernable. Thus, MicroCam is a standalone solution preserving privacy of the occupants.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

The Application of Convolutional Neural Networks (CNNs) to Recognize Defects in 3D-Printed Parts

Cracks and pores are two common defects in metallic additive manufacturing (AM) parts. In this paper, deep learning-based image analysis is performed for defect (cracks and pores) classification/detection based on SEM images of metallic AM parts. Three different levels of complexities, namely, defect classification, defect detection and defect image segmentation, are successfully achieved using a simple CNN model, the YOLOv4 model and the Detectron2 object detection library, respectively. The tuned CNN model can classify any single defect as either a crack or pore at almost 100% accuracy. The other two models can identify more than 90% of the cracks and pores in the testing images. In addition to the application of static image analysis, defect detection is also successfully applied on a video which mimics the AM process control images. The trained Detectron2 model can identify almost all the pores and cracks that exist in the original video. This study lays a foundation for future in situ process monitoring of the 3D printing process.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Effect of PVD-coated chromium on the subcooled flow boiling performance of nuclear reactor cladding materials

Here we elucidate the separate effect of a thin Cr coating deposited by physical vapor deposition (PVD) on the subcooled flow boiling performance of zircaloy-4. First, we run flow boiling experiments on prototypical zircaloy-4 surfaces mimicking the scratch pattern and surface roughness of nuclear reactor claddings. Then, we PVD-coat a 0.3 µm thick chromium layer on the same exact surface and repeat the same flow boiling investigations. All experiments are run using deionized water at atmospheric pressure, flowing on a 1 × 3 cm 2 rectangular cross section channel at a rate of 1000 kg/m 2 /s and a subcooling of 10 K. We measure the average temperature of the boiling surface at increasing surface heat fluxes, covering a wide range of heat transfer regimes, from single-phase forced convection to the boiling crisis. We also record high-speed videos of the boiling process, which we postprocess to measure bubble nucleation site density, growth time, departure diameter and frequency. The surface analysis reveals that, while the chromium coating does not seem change the surface roughness and morphology, it improves surface wettability. However, it decreases the critical heat flux. The chromium coating causes an increase of nucleation temperature, bubble departure diameter and growth time, and a reduction of the nucleation site density. The concurrence of these observations indicates that a size reduction of the nucleation sites, conformally covered by the chromium coating, may be the cause of the boiling performance deterioration. We confirm this hypothesis repeating the same analysis on a FeCrAl sample prepared and tested using the same protocol as the zircaloy-4 sample, but with a different initial surface texture.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Novel angular velocity estimation technique for plasma filaments

Magnetic field aligned filaments such as blobs and edge localized mode filaments carry significant amounts of heat and particles to the plasma facing components and they decrease their lifetime. The dynamics of these filaments determine at least a part of the heat and particle loads. These dynamics can be characterized by their translation and rotation. In this paper, we present an analysis method novel for fusion plasmas, which can estimate the angular velocity of the filaments on frame-by-frame time resolution. After pre-processing, the frames are two-dimensional (2D) Fourier-transformed, then the resulting 2D Fourier magnitude spectra are transformed to log-polar coordinates, and finally the 2D cross-correlation coefficient function (CCCF) is calculated between the consecutive frames. The displacement of the CCCF’s peak along the angular coordinate estimates the angle of rotation of the most intense structure in the frame. Further, the proposed angular velocity estimation method is tested and validated for its accuracy and robustness by applying it to rotating Gaussian-structures. The method is also applied to gas-puff imaging measurements of filaments in National Spherical Torus Experiment plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Optimization of a cyclone using MFIX and Nodeworks

Video depicting the optimization process of a cyclone on NETL's chemical looping reactor (CLR) using MFIX and Nodeworks. MFIX is used to model the cyclone using PIC. Nodeworks is then used to generate proposed geometry changes using a Latin hypercube. Each design is simulated, with an objective value being computed based on the cyclone efficiency and pressure drop. A Gaussian Process surrogate model is then constructed from the objective values. This surrogate model is then used by a differential evolution optimization algorithm to identify the optimal cyclone design. Details published here: Weber, J., Fullmer, W., Gel, A., and Musser, J. (February 4, 2020). "Optimization of a Cyclone Using Multiphase Flow Computational Fluid Dynamics." ASME. J. Fluids Eng. March 2020; 142(3): 031111. https://doi.org/10.1115/1.4045952 OSTI: https://www.osti.gov/pages/servlets/purl/1763893

cyclone↗
DOE OSTI2021

PixelStorm: A Remote Display for Remote Sensing Ground Stations

PixelStorm is a software application for displaying native high-performance applications from remote cloud environments. It is tailored for remote sensing missions that require high framerates, high resolutions, and minimal loss of quality. PixelStorm utilizes hardware-accelerated video compression on graphics processing units and a Sandia-developed streaming network protocol. Using our architecture, we can demonstrate interactive native applications running across two 4K monitors at 60 frames per second while maintaining the visual fidelity required by our missions. This technology allows for the migration of mission critical desktop applications to cloud environments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Large-area low-noise flexible organic photodiodes for detecting faint visible light

Silicon photodiodes are the foundation of light-detection technology; yet their rigid structure and limited area scaling at low cost hamper their use in several emerging applications. A detailed methodology for the characterization of organic photodiodes based on polymeric bulk heterojunctions reveals the influence that charge-collecting electrodes have on the electronic noise at low frequency. The performance of optimized organic photodiodes is found to rival that of low-noise silicon photodiodes in all metrics within the visible spectral range, except response time, which is still video-rate compatible. Solution-processed organic photodiodes offer several design opportunities exemplified in a biometric monitoring application that uses ring-shaped, large-area, flexible, organic photodiodes with silicon-level performance.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Droplet burning of n-butyl acetate/n-heptane mixtures in the standard atmosphere: experiments and numerical modeling

Blending petroleum fuels with oxygenates is a common approach to stem the depletion of crude oil while also mitigating the impact of their combustion on the environment. It has recently been considered that n-butyl acetate (BA, C6H12O2 , boiling point of 399K) could be a viable oxygenate additive to diesel fuel. In this application it is important to determine the influence of the fractional amount of BA on burning of the mixture. This presentation considers this problem from an experimental and computational approach using n-heptane (C7H14 , boiling point of 372K) as an essentially single component surrogate for diesel fuel. The burning configuration considered was ostensibly spherical symmetry as promoted by burning droplets under conditions where forced and natural convection effects were minimized. In this configuration the droplet and flame are concentric spheres and soot aggregates are trapped in a shell structure between the droplet and flame. Measurements were made of the droplet, flame and soot shell diameters through video imaging of the burning process. The experimental results were used to validate a detailed numerical model of the spherically symmetric droplet burning process that incorporated a consistent combustion kinetic mechanism for BA/heptane mixtures, comprised of 402 species and 16,872 reactions inclusive of soot chemistry. A model for soot formation was also included in the simulation to predict formation of the soot shell. The simulations agreed well with measured droplet and flame diameters. It was found both experimentally and computationally that adding BA to heptane had a minimal effect on the mixture burning rate, while the flame was positioned closer to the droplet surface and the simulated soot volume fraction decreased. The implications of these results are discussed for using BA as a potential additive to diesel fuel.

09 BIOMASS FUELS↗
DOE OSTI2020

Feature identification or classification using task-specific metadata

Innovations in the identification or classification of features in a data set are described, such as a data set representing measurements taken by a scientific instrument. For example, a task-specific processing component, such as a video encoder, is used to generate task-specific metadata. When the data set includes video frames, metadata can include information regarding motion of image elements between frames, or other differences between frames. A feature of the data set, such as an event, can be identified or classified based on the metadata. For example, an event can be identified when metadata for one or more elements of the data set exceed one or more threshold values. When the feature is identified or classified, an output, such as a display or notification, can be generated. Although the metadata may be useable to generate a task-specific output, such as compressed video data, the identifying or classifying is not used solely in production of, or the creation of an association with, the task-specific output.

Teuton, Jeremy R.↗
DOE OSTI2023

Parameters of the Lightning Attachment Processes in a Negative Cloud-To-Ground Stroke Observed on a Microsecond Timescale

We present time-correlated ultra-high-speed video camera and electromagnetic field measurements of the attachment processes in a natural negative cloud-to-ground stroke. The video camera frame exposure time and pixel resolution were 740 ns and 0.91 m/pixel, respectively. The common streamer zone (CSZ) was first observed 2.52 µs preceding the first frame showing the return stroke (RS) in progress, when the upward and downward leader-tips were 9.8 m apart. In the next frame, the two leaders were observed to have propagated toward each other within the CSZ, with their tips being 0.91 m apart. Our observations show with unprecedented precision/clarity that (a) the slow front in the field waveform is associated with the CSZ, and (b) the “proper” start of the RS is marked by the onset of the fast transition in the field waveform which occurs at the completion of the attachment processes (when the upward and downward leaders have merged).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Height-resolved emission spectroscopy and high-speed imaging of the TiAl6V4 vapor plume under laser powder bed fusion conditions

Optical emission spectroscopy is increasingly used as an in situ monitoring technique during laser powder bed fusion (LPBF) because plume emission holds elemental information not found in other in situ sensing techniques. This work explores the shape, stability, and temperature of the emission plume above the melt pool of Ti6Al4 V undergoing laser scans under LPBF-like processing conditions, using high-speed video and height-resolved spectroscopy to study the details of plume dynamics. Optical emission spectroscopy is conducted in the 480 nm to 525 nm region, where Ti emission is strong, with 0.3 mm vertical resolution above the baseplate. The Boltzmann plot method is used to determine temperature at each probed elevation, which indicates that the hottest location of the plume is occasionally elevated 0.3 mm to 0.6 mm above the scanning surface. The results show that the plume shape, stability, temperature, and spectra are highly dependent on the processing conditions. We highlight some of the complexities of optical emission spectroscopy and discuss potential challenges for implementing optical emission spectroscopy across an entire build.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Temporal and spatial tuning of optical constants in praseodymium doped ceria by electrochemical means

Abstract Temporal and spatial tuning of the refractive index of optical thin films is desired for flat optics applications. The redistribution of mobile ions in mixed ionic-electronic conductors (MIEC) has been demonstrated to serve as a viable means for achieving optical tuning down to the nanoscale. Here we studied the dynamic range of the optical tuning achievable in the refractive index, in the MIEC oxide – Pr x Ce 1− x O 2− δ (PCO), for x = 0.1, 0.2 and 0.4, at 500 °C, by in-situ spectrophotometry. Significant increases in the modulation of both the imaginary and real optical constants in the visible and the adjacent spectra were obtained for increased doping levels. Device employing an electrochemical titration method was implemented to modulate the oxygen concentration, and thereby the optical transmission of PCO. Incorporation of a patterned top electrode allowed for the demonstration of spatial control of PCO thin film properties by in-situ video imaging of the optical switching process. The electrochemically induced optical state is shown to remain non-volatile upon quenching the device to room temperature under applied bias.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Final Scientific/Technical Report: Real Time Particle Size and Settling Velocity for In-Situ Monitoring of Deep-Sea Polymetallic Nodule Mining

In the coming decade, it is anticipated that deep-sea mining activities will commence throughout the deep ocean, at depths around 4000-6000m where vast deposits of baseball-sized rocks called polymetallic nodules lay on the seabed. These nodules contain resources such as cobalt, copper and nickel which are needed to produce batteries for electric vehicles. Collector machines driving on the seabed and gathering these nodules will stir up clouds of sediment, and there is concern about how the resulting concentration of sediment in the near-bottom ocean water will affect deep and mid-ocean biology. There is currently no technology capable of comprehensively monitoring the concentrations and properties of stirred up sediment in the deep. This project addressed this critical gap by developing a deep-sea particle measurement system (Real-Time Size and Settling Velocity, RTSSV) that uses a multi-camera high-resolution video imaging system, with accompanying image processing, to measure the concentration, size distribution and settling speed of sediment. Importantly, these measurements are made in-situ, so that delicate sediment aggregates will not be disturbed, as is the case when collecting samples at depth to be measured in the lab. The instrument can be mounted on a variety of established deep-sea platforms such as moorings, AUVs and ROVs, providing data in near real time, where the key measurements need to be made—from the source of the sediment disturbance and throughout the resulting plume—vital for determining if deep-sea mining can be done in an environmentally responsible manner. The developed system (RTSSV) will possess the only such capability worldwide, giving a US-based group a substantial competitive advantage as this newly evolving global industry develops. As added value, the technology will also have widespread application to the offshore oil and gas industry, dredging industry, and to sediment science research.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

3DBFSVBF (3D BatFinder Smart Video BioFilter and Multi-class BatFinder Smart Video BioFilter) [SWR-22-88]

Bats are notoriously difficult to study, therefore, identifying specific behavioral trends and the precise environmental conditions at the time of collision requires a monitoring solution that can reliably collect relevant data. To date, thermal infrared video surveillance has been extensively applied to study bats and has proven to be a powerful yet cumbersome tool. Current analytical approaches are time consuming because data processing data has not been fully automated. In the past, steps have been taken to record avian and bat activity in conjunction with complicated image processing techniques that separate species from other moving objects within the field of view (i.e. clouds and portions of the wind turbine). Once the videos are collected, the post-processing does not allow real time monitoring and identification, leading to a delay in both studying the behavior of these species and determining the effectiveness of any impact reduction strategy being studied. Moreover, object identification capability is lacking, thus limiting the usefulness of video data. To resolve these issues, we are using open source 3D computer vision and machine learning techniques allowing for automatic detection of objects in real-time with the ability to correlate these objects with environmental variables and recording the flight paths of each object. The machine learning has been trained on 3D data and allows for automated real-time data collection, identification and tracking, thereby eliminating the need for long and tedious post-analysis processing of the videos. This machine learning model is an added feature to the previous BatFinder Smart Video BioFilter and increases the accuracy of that systems classification by increasing the accuracy of identifying bats (90% accuracy) and insects (69% accuracy) to a 97% accuracy. There are two object classifier machine learning models, Binary and multi-classification. Binary object classifier labeled BatFinder_Smart_Video_BioFilter.h5 distinguishes between biological objects and non-biological objects. The main goal of this object classifier is to ignore the turbine blades while detecting biological object flying withing the rotor swept area of the turbine. Non-biological objects have a probability of 0 and biological objects have a probability of 1. Multi-classifier labeled Multiclass_BatFinder_Smart_Video_BioFilter.h5 distinguishes between bats, birds, insects and non-biological.

Yarbrough, John↗
DOE OSTI2022

BFSVBF (BatFinder Smart Video BioFilter) [SWR-22-87] and Multi-class BatFinder Smart Video BioFilter Keras

Bats are notoriously difficult to study, therefore, identifying specific behavioral trends and the precise environmental conditions at the time of collision requires a monitoring solution that can reliably collect relevant data. To date, thermal infrared video surveillance has been extensively applied to study bats and has proven to be a powerful yet cumbersome tool. Current analytical approaches are time consuming because data processing data has not been fully automated. In the past, steps have been taken to record avian and bat activity in conjunction with complicated image processing techniques that separate species from other moving objects within the field of view (i.e. clouds and portions of the wind turbine). Once the videos are collected, the post-processing does not allow real time monitoring and identification, leading to a delay in both studying the behavior of these species and determining the effectiveness of any impact reduction strategy being studied. Moreover, object identification capability is lacking, thus limiting the usefulness of video data. To resolve these issues, we are using open source computer vision and machine learning techniques allowing for automatic detection of objects in real-time with the ability to correlate these objects with environmental variables and recording the flight paths of each object. The code has gone through five rounds of development with images used to train the models. This advancement allows for automated real-time data collection, identification, and tracking, thereby eliminating the need for long and tedious post-analysis processing of the videos. We will discuss the two open source and publicly available machine learning models developed within this scope of this work: 1) a binary model with a 97.5% accuracy in identifying the difference between an object and an empty scene, including wind turbine and clouds; and 2) a multiple classification model with the capability of identifying the type of object detected: bats (90% accuracy), birds (83% accuracy), insects (69% accuracy) and non-biological (99% accuracy).

Yarbrough, John↗
DOE OSTI2021

Imaging pyrometry for most color cameras using a triple pass filter

A simple combination of the Planck blackbody emission law, optical filters, and digital image processing is demonstrated to enable most commercial color cameras (still and video) to be used as an imaging pyrometer for flames and explosions. The hardware and data processing described take advantage of the color filter array (CFA) that is deposited on the surface of the light sensor array present in most digital color cameras. In this work, a triple-pass optical filter incorporated into the camera lens allows light in three 10-nm wide bandpass regions to reach the CFA/light sensor array. These bandpass regions are centered over the maxima in the blue, green, and red transmission regions of the CFA, minimizing the spectral overlap of these regions normally present. A computer algorithm is used to retrieve the blue, green, and red image matrices from camera memory and correct for remaining spectral overlap. A second algorithm calibrates the corrected intensities to a gray body emitter of known temperature, producing a color intensity correction factor for the camera/filter system. The Wien approximation to the Planck blackbody emission law is used to construct temperature images from the three color (blue, green, red) matrices. A short pass filter set eliminates light of wavelengths longer than 750 nm, providing reasonable accuracy (±10%) for temperatures between 1200 and 6000 K. The effectiveness of this system is demonstrated by measuring the temperature of several systems for which the temperature is known.

47 OTHER INSTRUMENTATION↗