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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Imaging of surface nanobubbles by atomic force microscopy in liquids: Influence of drive frequency on the characterization of ultrasoft matter: Surface Nanobubbles and Soft Matter by AFM

Imaging of soft matter with atomic force microscopy (AFM) is challenging due to tip-induced deformation, which convolutes with the measurement. The challenges are generally more serious in liquid environments due to a severe loss of sensitivity of the vibrating microcantilever to external forces, as well as due to the presence of undesirable mechanical resonances when piezoelectric excitation systems are used. Furthermore, the choice of imaging parameters can have a significant impact on the quality of the results, such that the customary practices used for tuning the cantilever are not always appropriate. Here we explore the influence of the chosen drive frequency on the imaging of ultrasoft matter, using surface gas nanobubbles on gold-coated glass and highly oriented pyrolytic graphite (HOPG) as a test platform. We carry out single- and multifrequency AFM experiments using both the traditional amplitude-peak method and the recently proposed phase-slope-peak method for tuning the cantilever, as well as piezoelectric and photothermal excitation, providing an extensive discussion on the factors governing the level of tip-induced sample deformation and the quality of the phase contrast obtained for each of the methods. The general conclusion is that there is no “one-size-fits-all” approach for tuning the cantilever for low-impact tapping-mode AFM, although rational optimization of the imaging process is generally possible, whereby the choice of the drive frequency plays a prominent role. The physical insight and guidelines provided here can be extremely useful for the gentle imaging of a wide range of biological and other soft materials in liquid environments. Microsc. Res. Tech. 80:41–49, 2017.

42 ENGINEERING↗
DOE OSTI2017

An Artificial Retina Processor for Track Reconstruction at the LHC Crossing Rate

The goal of the INFN-RETINA R&D project is to develop and implement a computational methodology that allows to reconstruct events with a large number (> 100) of charged-particle tracks in pixel and silicon strip detectors at 40 MHz, thus matching the requirements for processing LHC events at the full bunch-crossing frequency. Our approach relies on a parallel pattern-recognition algorithm, dubbed artificial retina, inspired by the early stages of image processing by the brain. In order to demonstrate that a track-processing system based on this algorithm is feasible, we built a sizable prototype of a tracking processor tuned to 3 000 patterns, based on already existing readout boards equipped with Altera Stratix III FPGAs. The detailed geometry and charged-particle activity of a large tracking detector currently in operation are used to assess its performances. We report on the test results with such a prototype.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Seeing the whole picture: Methods for getting the most from micro X-ray computed tomography of TRISO nuclear fuel particles

Tristructural isotropic (TRISO) coated fuel particles are a nuclear fuel form under extensive study for use in advanced nuclear reactor concepts. TRISO fuels are subjected to high temperature neutron irradiations and then examined to assess their performance by determining fission product retention and studying morphological changes. Micro X-ray computed tomography is one method of nondestructively studying the effects of TRISO performance. This work addresses the need for image processing to remove X-ray tomographic reconstruction artifacts that prevent the study of TRISO features, as the TRISO particles’ high Z kernel can introduce metal artifacts that degrade the image quality in the surrounding low Z coating layers. These metal artifacts were reduced by imaging the TRISO particles with both high- and low-energy X-rays and applying a mask to the radiographs obtained with low-energy X-rays to digitally remove the dense fuel kernel region. These masked radiographs were then used to produce a tomographic reconstruction which was combined with the tomographic reconstruction of the high-energy data. This enabled the relatively-low-density TRISO buffer layer to be examined in more detail, providing information on irradiation induced dimensional changes of the coatings. This methodology, which helps see the full picture of a TRISO particle, is not limited to nuclear fuels but can be applied to systems that contain highly attenuating material surrounded by less dense materials.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Challenges in solving structures from radiation-damaged tomograms of protein nanocrystals assessed by simulation

Structure-determination methods are needed to resolve the atomic details that underlie protein function. X-ray crystallography has provided most of our knowledge of protein structure, but is constrained by the need for large, well ordered crystals and the loss of phase information. The rapidly developing methods of serial femtosecond crystallography, micro-electron diffraction and single-particle reconstruction circumvent the first of these limitations by enabling data collection from nanocrystals or purified proteins. However, the first two methods also suffer from the phase problem, while many proteins fall below the molecular-weight threshold required for single-particle reconstruction. Cryo-electron tomography of protein nanocrystals has the potential to overcome these obstacles of mainstream structure-determination methods. Here, a data-processing scheme is presented that combines routines from X-ray crystallography and new algorithms that have been developed to solve structures from tomograms of nanocrystals. This pipeline handles image-processing challenges specific to tomographic sampling of periodic specimens and is validated using simulated crystals. The tolerance of this workflow to the effects of radiation damage is also assessed. The simulations indicate a trade-off between a wider tilt range to facilitate merging data from multiple tomograms and a smaller tilt increment to improve phase accuracy. Since phase errors, but not merging errors, can be overcome with additional data sets, these results recommend distributing the dose over a wide angular range rather than using a finer sampling interval to solve the protein structure.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

PANTHER. Pattern ANalytics To support High-performance Exploitation and Reasoning.

Sandia has approached the analysis of big datasets with an integrated methodology that uses computer science, image processing, and human factors to exploit critical patterns and relationships in large datasets despite the variety and rapidity of information. The work is part of a three-year LDRD Grand Challenge called PANTHER (Pattern ANalytics To support High-performance Exploitation and Reasoning). To maximize data analysis capability, Sandia pursued scientific advances across three key technical domains: (1) geospatial-temporal feature extraction via image segmentation and classification; (2) geospatial-temporal analysis capabilities tailored to identify and process new signatures more efficiently; and (3) domain- relevant models of human perception and cognition informing the design of analytic systems. Our integrated results include advances in geographical information systems (GIS) in which we discover activity patterns in noisy, spatial-temporal datasets using geospatial-temporal semantic graphs. We employed computational geometry and machine learning to allow us to extract and predict spatial-temporal patterns and outliers from large aircraft and maritime trajectory datasets. We automatically extracted static and ephemeral features from real, noisy synthetic aperture radar imagery for ingestion into a geospatial-temporal semantic graph. We worked with analysts and investigated analytic workflows to (1) determine how experiential knowledge evolves and is deployed in high-demand, high-throughput visual search workflows, and (2) better understand visual search performance and attention. Through PANTHER, Sandia's fundamental rethinking of key aspects of geospatial data analysis permits the extraction of much richer information from large amounts of data. The project results enable analysts to examine mountains of historical and current data that would otherwise go untouched, while also gaining meaningful, measurable, and defensible insights into overlooked relationships and patterns. The capability is directly relevant to the nation's nonproliferation remote-sensing activities and has broad national security applications for military and intelligence- gathering organizations.

58 GEOSCIENCES↗
DOE OSTI2022

Pore-scale observations of natural hydrate-bearing sediments via pressure core sub-coring and micro-CT scanning

Abstract Both intra-pore hydrate morphology and inter-pore hydrate distribution influence the physical properties of hydrate-bearing sediments, yet there has been no pore-scale observations of hydrate habit under pressure in preserved pressure core samples so far. We present for the first time a pore-scale micro-CT study of natural hydrate-bearing cores that were acquired from Green Canyon Block 955 in UT-GOM2-1 Expedition and preserved within hydrate pressure–temperature stability conditions throughout sub-sampling and imaging processes. Measured hydrate saturation in the sub-samples, taken from units expected to have in-situ saturation of 80% or more, ranges from 3 ± 1% to 56 ± 11% as interpreted from micro-CT images. Pore-scale observations of gas hydrate in the sub-samples suggest that hydrate in silty sediments at the Gulf of Mexico is pore-invasive rather than particle displacive, and hydrate particles in these natural water-saturated samples are pore-filling with no evidence of grain-coating. Hydrate can form a connected 3D network and provide mechanical support for the sediments even without cementation. The technical breakthrough to directly visualize particle-level hydrate pore habits in natural sediments reported here sheds light on future investigations of pressure- and temperature-sensitive processes including hydrate-bearing sediments, dissolved gases, and other biochemical processes in the deep-sea environment.

58 GEOSCIENCES↗
DOE OSTI2019

Progress Update on Iterative Reconstruction of Neutron Tomographic Images, 2019

The fiscal year (FY) 2019 update of this report satisfies the technical deliverable “Simultaneous reconstruction of simultaneous observables” for project OR16-ML-3DTomography-PD3Jb, “3D Tomography and Image Processing Using Fast Neutrons.” At its inception, this project had two overall goals. The first of these goals was to extend associated-particle fast neutron transmission and, particularly, induced-reaction tomographic imaging algorithms to three dimensions. The second of these goals was to automatically segment the resultant tomographic images into constituent parts and then extract information about the parts, such as the class of shape and potentially shape parameters. This report addresses the component of the project concerned with three-dimensional (3D) image reconstruction and represents the cumulative progress to date. In particular, it addresses the ability to decompose neutrons arriving at times consistent with transmission into transmission and small-angle scattering. Because incremental progress in each year builds on the previous year’s progress and end-ofyear status, progress is most usefully recorded in a cumulative document where incremental progress is reported on a year-by-year basis. Consequently, this report constitutes an update of the last 3 years’ reports [1, 2, 14]. The present year’s progress is detailed in Section 7.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

A Quantum Leap for Dynamic Radiography

Dynamic radiography techniques have been instrumental in advancing the National Nuclear Security Administration's (NNSA) mission since WWII. This paper explores the transformative potential of quantum information science (QIS) to revolutionize dynamic radiography through enhanced image processing, tomographic reconstruction, statistics and uncertainty quantification (UQ), and artificial intelligence integration. By leveraging quantum algorithms to extract previously inaccessible information from existing datasets, this interdisciplinary approach promises unprecedented insights at the intersection of dynamic imaging, artificial intelligence, and quantum information technologies.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

DEEP CELLULAR RECURRENT NEURAL ARCHITECTURE FOR EFFICIENT MULTIDIMENSIONAL TIME-SERIES DATA PROCESSING

Efficient processing of time series data is a fundamental yet challenging problem in pattern recognition. Though recent developments in machine learning and deep learning have enabled remarkable improvements in processing large scale datasets in many application domains, most are designed and regulated to handle inputs that are static in time. Many real-world data, such as in biomedical, surveillance and security, financial, manufacturing and engineering applications, are rarely static in time, and demand models able to recognize patterns in both space and time. Current machine learning (ML) and deep learning (DL) models adapted for time series processing tend to grow in complexity and size to accommodate the additional dimensionality of time. Specifically, the biologically inspired learning based models known as artificial neural networks that have shown extraordinary success in pattern recognition, tend to grow prohibitively large and cumbersome in the presence of large scale multi-dimensional time series biomedical data such as EEG. Consequently, this work aims to develop representative ML and DL models for robust and efficient large scale time series processing. First, we design a novel ML pipeline with efficient feature engineering to process a large scale multi-channel scalp EEG dataset for automated detection of epileptic seizures. With the use of a sophisticated yet computationally efficient time-frequency analysis technique known as harmonic wavelet packet transform and an efficient self-similarity computation based on fractal dimension, we achieve state-of-the-art performance for automated seizure detection in EEG data. Subsequently, we investigate the development of a novel efficient deep recurrent learning model for large scale time series processing. For this, we first study the functionality and training of a biologically inspired neural network architecture known as cellular simultaneous recurrent neural network (CSRN). We obtain a generalization of this network for multiple topological image processing tasks and investigate the learning efficacy of the complex cellular architecture using several state-of-the?art training methods. Finally, we develop a novel deep cellular recurrent neural network (CDRNN) architecture based on the biologically inspired distributed processing used in CSRN for processing time series data. The proposed DCRNN leverages the cellular recurrent architecture to promote extensive weight sharing and efficient, individualized, synchronous processing of multi-source time series data. Experiments on a large scale multi-channel scalp EEG, and a machine fault detection dataset show that the proposed DCRNN offers state-of-the-art recognition performance while using substantially fewer trainable recurrent units.

Vidyaratne, Lasitha S.↗
DOE OSTI2020

Impacts of advanced diesel combustion operation and fuel formulation on soot nanostructure and reactivity

Advanced diesel combustion, accomplished via a single pulse fuel injection and high levels of exhaust gas recirculation (referred to as “PCCI”, partially-premixed charge compression ignition), is shown to be a path to reduce oxides of nitrogen and particulate matter simultaneously. This is well established in the literature. Less established is the extent to which such dilute combustion processes influence soot formation and affect soot that is emitted from diesel engines under such combustion modes. This work focuses on characterization of the nanostructure and oxidative reactivity of soot generated by a light-duty turbodiesel engine operating under a PCCI combustion mode, a dilute, low-temperature combustion process. Previous work on a type of PCCI combustion, referred to as high-efficiency clean combustion (HECC), showed soot samples having a fullerenic nanostructure, characterized by high levels of tortuosity of the fringe layers as seen in transmission electron micrograph images and as quantified using an image processing algorithm. Thermogravimetric analysis of the HECC mode soot samples showed that they displayed higher rates of oxidation than soot samples from a conventional diesel combustion mode. The present work returns to PCCI combustion, considering the timing of the main fuel injection, and the effects of operating on fuels rich in n-alkanes, particularly a fuel produced from a low temperature Fischer-Tropsch process (LTFT) and a renewable diesel fuel (RD) produced via hydrodeoxygenation of a plant oil. PCCI combustion conditions yield soot that shows higher reactivity compared to soot from the conventional combustion mode regardless of fuel type. LTFT and RD fuels produce soots with lower reactivity compared to ULSD. Soots produced from PCCI combustion have higher surface oxygen concentration and higher proportion of amorphous carbon. In addition, TEM images show that PCCI soots from all three fuels have smaller primary particle and particle aggregate sizes, and smaller graphene layers. These properties explain the higher reactivity of soot from PCCI combustion. The less reactive soots, which are produced from LTFT and RD fuel under conventional combustion, show internal burning during oxidation. However, soots with higher reactivity which are produced from late injection PCCI combustion and ULSD show shrinking core oxidation, most likely because of their overall amorphous structure.

42 ENGINEERING↗
DOE OSTI2018

A sub-sampled approach to extremely low-dose STEM

The inpainting of deliberately and randomly sub-sampled images offers a potential means to image specimens at a high resolution and under extremely low-dose conditions (≤1 e - /Å 2 ) using a scanning transmission electron microscope. We show that deliberate sub-sampling acquires images at least an order of magnitude faster than conventional low-dose methods for an equivalent electron dose. More importantly, when adaptive sub-sampling is implemented to acquire the images, there is a significant increase in the resolution and sensitivity which accompanies the increase in imaging speed. Lastly, we demonstrate the potential of this method for beam sensitive materials and in-situ observations by experimentally imaging the node distribution in a metal-organic framework.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Automated detection of rear contact voids in perc cells with photoluminescence imaging

An image-processing method combined with Fourier analysis is introduced here in this work as a rapid and highly-automated method of detecting rear contact voids in passivated emitter and rear contact cells (PERC). This approach utilizes photoluminescence (PL) imaging to locate the central point of the most detrimental type of voids and associate a void fraction to each cell in a manner of seconds. Acoustic microscopy and scanning electron microscopy are also used as complementary tools to confirm the presence of voids detection using PL imaging and provide more insight into the actual physical mechanism of what is observed in PL measurements.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Distributed memory parallel Markov random fields using graph partitioning

Markov random fields (MRF) based algorithms have attracted a large amount of interest in image analysis due to their ability to exploit contextual information about data. Image data generated by experimental facilities, though, continues to grow larger and more complex, making it more difficult to analyze in a reasonable amount of time. Applying image processing algorithms to large datasets requires alternative approaches to circumvent performance problems. Aiming to provide scientists with a new tool to recover valuable information from such datasets, we developed a general purpose distributed memory parallel MRF-based image analysis framework (MPI-PMRF). MPI-PMRF overcomes performance and memory limitations by distributing data and computations across processors. The proposed approach was successfully tested with synthetic and experimental datasets. Additionally, the performance of the MPI-PMRF framework is analyzed through a detailed scalability study. We show that a performance increase is obtained while maintaining an accuracy of the segmentation results higher than 98%. The contributions of this paper are: (a) development of a distributed memory MRF framework; (b) measurement of the performance increase of the proposed approach; (c) verification of segmentation accuracy in both synthetic and experimental, real-world datasets

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

WE-D-204-02: Novel Method for Correcting Degradation of Sharpness of Liquid-Crystal Display Based On Modulation Transfer Function

Purpose: To develop a method for improving sharpness of images reproduced on liquid-crystal displays (LCDs) by compensating for the degradation of modulation transfer function (MTF) of the LCD. Methods: The inherent MTF of a color LCD (display MTF) was measured using a commercially available color digital camera. The frequency responses necessary to compensate for the resolution property of the LCD were calculated from the inverses of the display MTFs in both the horizontal and vertical directions. In addition, the inverses of the display MTFs were combined with the response of the human eye. The finite impulse response (FIR) filters were computed by taking the inverse Fourier transform of the frequency responses, and the effects of the FIR filtering on both the resolution and noise properties of the displayed images were verified by measuring the MTF and Wiener spectrum (WS), respectively. The FIR filtering was then applied to the representation of digital bone and chest radiographs. Results: The FIR filtering improved the MTF values by up to almost 1.0 or greater over the frequency range of interest, while it minimally increased the WS values. Combining the inverses of the display MTFs with the response of the human eye led to further refinement of the MTF. Our method was successfully and beneficially applied to the image interpretation of bone radiographs. The resolution enhancement of chest radiographs, which include larger scattered radiation than bone radiographs, was easily perceived by incorporating the response of the human eye. In addition, no artifacts were observed on the processed images. Conclusion: Our proposed method to compensate for the degradation of the resolution properties of LCDs has the potential to improve the observer performance of radiologists when reading digital radiographs. This work was supported in part by grant from EIZO Corporation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

In situ Detection of Plasma Induced Surface Interaction based on Deep Learning based Visual Diagnostics (Technical Report)

It is characteristic for many plasma devices to undergo plasma-material interaction leading to surface erosion. These processes, often not easily detectable, lead to changes in device performance and lifespan. State-of-the-art lifetime tests and wear experiments require over 1000s hours. A self-consistent model for accurately predicting the erosion's effects is not available. In situ detection of these processes is not a trivial task since the surface variations at the early stages have a micron scale. Such limitations not only restrict testing and prediction capabilities but also slow the development of new thrusters and limit mission duration. To address these challenges, an in-situ diagnostic for real-time erosion assessment has been developed, aiming to expedite lifetime testing and broaden experimental campaigns. Several works were dedicated to real-time and in situ monitoring of material erosion during plasma exposure using laser holography, microscopy, and with telemicroscopes. However, the applicability of these approaches is limited due to complexity, cost and less flexibility as they often require placing diagnostic equipment inside the vacuum chamber. In collaboration with Princeton Collaborative Research Facility (PCRF), Princeton Plasma Physics Laboratory (PPPL), a new diagnostic approach is developed, where geometry modifications to the ceramic channel walls were introduced that would result in accelerated channel erosion. We employed Long-distance microscope (LDM) imagery, combined with Deep-Learning based Shape from focus or depth from focus (DFF or SFF) approach, that provides an accessible and cost-effective solution. LDM employs focus variation techniques to continuously capture multiple images of the target object at distinct focal planes. DFF, an optical focus variation method, generates a 3D topographical surface depth map from a sequence of variably focused images. Combined with the developed diagnostic, this approach offers a controllable means to study erosion under accelerated conditions. In this work, we develop Neural Network-based DFF algorithm applicable for LDM data to quantitatively evaluate plasma induced surface modification from LDM data. Next, we develop Deep Learning-based super-resolution depth map image reconstruction technique to increase the resolution of depth maps obtained from DFF algorithm to improve the accuracy of erosion measurements. Thirdly, we develop several image processing techniques to remove noise and improve the quality of depth map image. Here we report the results of initial tests for this approach. An experimental setup designed and built in PPPL was employed that consists of a 3-cm gridded ion source that produces a neutralized argon beam with energies up to 600 eV. A hexagonal boron nitride (h-BN) ceramic target, designed based on computational predictions, was used. Tests were conducted to reconstruct the complex geometry of the target under the lighting conditions of the operated ion source.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Impacts of advanced diesel combustion operation on soot nanostructure and reactivity

Advanced diesel combustion, accomplished via a single pulse fuel injection and high levels of exhaust gas recirculation, is shown to be a path to reduce oxides of nitrogen and particulate matter simultaneously. This is well established in the literature. Less established is how well such dilute combustion processes influence soot formation and affect soot that is emitted from diesel engines under such combustion modes. This work focuses on characterization of the nanostructure and oxidative reactivity of soot generated by a light-duty turbodiesel engine operating under a dilute, low-temperature combustion process referred to as high-efficiency clean combustion. The high-efficiency clean combustion soot samples are shown to have a fullerenic nanostructure, characterized by high levels of tortuosity of the fringe layers as seen in transmission electron micrograph images and as quantified using an image processing algorithm. Thermogravimetric analysis of the high-efficiency clean combustion soot samples shows that they have higher rates of oxidation than soot samples from a conventional diesel combustion mode. The linkage between the nanostructure of the high-efficiency clean combustion soot samples and their oxidative reactivity reinforces and supports the structure–property relationship for soot that greater curvature on the soot nanostructure leads to significant increases in oxidative reactivity.

Yehliu, Kuen↗
DOE OSTI2023

Automated Progress Monitoring in Modular Construction Factories Using Computer Vision and Building Information Modeling

Modular construction methods have recently gained interest due to the advantages offered in terms of safety, quality, and productivity for projects. In this method, a significant portion of the construction is performed off-site in factories where modular components are built in different workstations, assembled on the production line, and shipped to the site for installation. Due to the labor-intensive nature of tasks, cycle times in modular construction factories are highly variable, which commonly leads to major bottlenecks and delays in construction projects. To remedy this effect, recent methods rely on sensors such as RFID to monitor the production process, which is reportedly expensive, and intrusive to the work process. Recently, computer vision-based methods have been proposed to track the production process in modular construction factories. However, these methods overlook monitoring the assembly process on the production line. Therefore, this paper presents a method to monitor the assembly process by integrating computer vision-based methods with Building Information Modeling (BIM). The proposed method detects the modular units using object segmentation; superimposes the installation area with the corresponding 2D region using BIM, and identifies the installation of the components using image processing techniques. The proposed method has been validated using surveillance videos captured from a modular construction factory in the US. Successful implementation of the proposed method can lead to timely identification of delays during the assembly process and reduce delays in modular integrated construction projects.

building information modeling↗
DOE OSTI2017

Oak Ridge National Laboratory Progress Update on Segmentation of Neutron Tomographic Images, 2017

The present report documents progress regarding segmentation of fast-neutron tomographic images of objects consisting of assemblies of machined parts with distinct boundaries. This report satisfies the fiscal year 2017 technical deliverable for project OR16-3DTomography-PD3Jb, “3D Tomography and Image Processing Using Fast Neutrons,” to report on the extension of geometric shape-finding algorithms to three dimensions. The project has two overall goals. The first of these goals is to extend associated particle fast neutron transmission and, particularly, induced-reaction tomographic imaging algorithms to three dimensions. The second of these goals is to automatically segment the resultant tomographic images into constituent parts, and then extract information about the parts, such as the class of shape and potentially the shape parameters. This report addresses continued progress in the component of the project concerned with image segmentation.

3D tomography↗