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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

3D imaging techniques for characterising microcracks in cement-based materials

Concrete inherently contains pores and microcracks that can adversely impact its mechanical properties and long-term durability. However, characterising microcracks is difficult due to their complex, multiscale and three-dimensional (3D) nature. This paper presents an evaluation of 3D imaging techniques for characterising microcracks induced by different mechanisms. Seven cement pastes, mortars and concretes subjected to drying shrinkage, autogenous shrinkage and freeze-thaw cycles were investigated using focused ion beam nanotomography (FIB-nt), broad ion beam serial section tomography (BIB-SST), laser scanning confocal microscopy (LSCM) combined with serial sectioning and X-ray microtomography (μCT). The study shows that the characteristics of microcracks vary significantly depending on exposure conditions. Yet there is no single technique that can capture the entire size range of microcracks from sub to tens of μm within a sufficiently representative sampling volume. The achievable image volume and resolution, and the advantages and disadvantages of each technique are compared and discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Experiments and CFD simulation of mass transfer and hydrodynamics in a cylindrical bubble column

Flow visualization, Particle Image Velocimetry (PIV) and dissolved oxygen charging experiments are carried out in a small-scale cylindrical air-water bubble column at different superficial gas velocities. Void fraction profile, total gas holdup, liquid phase velocity field and volumetric mass transfer coefficient are obtained. Details of each experiment are given, with a new procedure for obtaining a 2D void fraction profile from flow visualization by image processing and time averaging which could provide useful information for CFD model validation. The experimental results are analyzed and compared with simulations where good agreement is found for both local and global flow patterns. The averaged governing equations and constitutive relations used in the simulation are discussed briefly, with emphasis on the proper comparison between simulations and experiments based on the derivation of the two-fluid model. Finally, by conducting three sets of experiments related to phase distribution, velocity field and mass transfer in the same apparatus, a validated CFD model for multiphase mass transfer is established and can be used in the future design of a multi-phase reactor.

42 ENGINEERING↗
DOE OSTI2019

In situ observations of reacting Al/Fe 2 O 3 thermite: Relating dynamic particle size to macroscopic burn time

Time-resolved x-ray imaging is used to capture high-resolution images of the Al/Fe 2 O 3 thermite reaction as it proceeds in an extended burn tube. The observed thermite reaction products are generally large, multi-phase composite particles. Image processing algorithms are developed to extract a continuous record of composite particle size as a function of time and distance from the ignition point. Small particles are observed immediately after ignition, followed by a rapid increase in particle size and a gradual decline toward late times. Correlating the dynamic particle size with visible light emission from the reaction, we propose a two-stage mechanism where the combination of early ignition and delayed expansion causes the material closest to the ignition point to undergo substantial coalescence before it begins moving down the tube. We also identify two mechanisms that may contribute to particle size refinement at late times, both based on the collapse of large gas bubbles that are observed preferentially in particles at earlier times. A new approach to extracting burn times from luminous intensity data is explored, and we find that a more nuanced analysis of burn time and luminous intensity may help to improve reactivity assessments when data from in situ studies is unavailable.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Particle clustering effects on damage mechanisms in elastomeric syntactic foams

To elucidate the damage mechanisms in syntactic foams with hollow glass microballoon (GMB) reinforcement and elastomer matrices, in situ X-ray computed tomography mechanical testing was performed on syntactic foams with increasing GMB volume fraction. Image processing and digital volume correlation techniques identified very different damage mechanisms compared to syntactic foams with brittle matrices. Specifically, the prevailing mechanism transitioned from dispersed GMB collapse at low volume fraction to clustered GMB collapse at high volume fraction. Moreover, damage initiated and propagated earlier in closely-packed GMBs for all specimens. Both of these trends were attributed to increased interaction between closely-packed GMBs. This was validated by statistical analysis of GMB damage, which identified a consistent, inverse relationship among the probability of survival and the local coordination number (N neighbor ) across all specimens.

42 ENGINEERING↗
DOE OSTI2022

Experimental study of crystallization fouling by calcium carbonate: Effects of surface structure and material

We report mitigation of crystallization fouling on heat transfer surface is crucial for developing highly efficient desalination techniques, because the tenacious and porous crystal layer can drastically deteriorate system efficiency. Surface modification is a potential approach to decrease scale formation and promote crystal removal, but some of the mechanisms between crystallization and substrate properties are still not fully understood. In this study, we use dual-angle microscopic visualization and image processing to quantify three-dimensional crystallization behavior and analyze the impact of surface structure and material. The transition of crystal size and distance with roughness and wettability are identified from statistical analysis. The determination of crystal porosity and effective thermal conductivity is enabled by visualized geometry and measured deposition mass. This study shed lights on potential strategies of surface modification to mitigate scaling in desalination by enhancing scale removal and localizing crystal growth.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

A novel occupancy detection solution using low-power IR-FPA based wireless occupancy sensor

Significant energy savings can be achieved by operating heating, ventilation, and air conditioning control systems with indoor occupancy measurement information. This paper presents a novel plug-and-play occupancy sensing method which will enable temporal minimization of building energy consumption to meet building usage behavior without privacy concerns. The proposed wireless occupancy sensing platform is based on long-wave infra-red (LWIR) focal-plane arrays (FPAs), or thermal imagers, that detect thermal energy rather than visible light. We developed an advanced sensor package consisting of multiple thermal imagers with low-cost optical enhancements to increase field of view and increase sensitivity to occupant detection (filtering building clutter). These imagers can be coupled with radio frequency and ultrasonic-based radar to enhance data collection at key occupant zone boundaries to improve accuracy. Standard filtering and estimation techniques from the image processing and computer vision communities are introduced to overcome the accuracy issues suffered by traditional PIR based sensing, especially when occupants remain relatively still. Accurate low-level counting of individuals can be achieved with minimal impact on privacy. As a result, the proposed occupancy detection method can work as a retrofit to enable real-time understanding of the building operational state and usage behavior. Sensor data obtained from real test building zones was used to test the efficacy of the method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Physics-informed semantic inpainting: Application to geostatistical modeling

A fundamental problem in geostatistical modeling is to infer the heterogeneous geological field based on limited measurements and some prior spatial statistics. Semantic inpainting, a technique for image processing using deep generative models, has been recently applied for this purpose, demonstrating its effectiveness in dealing with complex spatial patterns. However, the original semantic inpainting framework incorporates only information from direct measurements, while in geostatistics indirect measurements are often plentiful. In this work, to overcome this limitation, we propose a physics-informed semantic inpainting framework, employing the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) and jointly incorporating the direct and indirect measurements by exploiting the underlying physical laws. Our simulation results for a high-dimensional problem with 512 dimensions show that in the new method, the physical conservation laws are satisfied and contribute in enhancing the inpainting performance compared to using only the direct measurements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Advanced detector signal acquisition and electron beam scanning for high resolution SEM imaging

We report the advancement of materials science at the mesoscale requires improvements in both sampling volumes/areas and spatial resolution in order to make statistically significant measurements of microstructures that influence higher-order material properties, such as fatigue and fracture. Therefore, SEM-based techniques have become desirable due to improvements in imaging resolution, large sample handling capability, and flexibility for in-situ instrumentation. By using fast sampling of SEM electron detector signals, intrinsic beam scanning defects have been identified that are related to the response time of the SEM electron beam deflectors and electron detectors. Mitigation of these beam scanning defects using detector sampling approaches and an adaptive model for settling time is shown to produce higher resolution SEM images, at faster image acquisition times, with a means to quantify the different response functions for various beam deflectors and detectors including those for electrons and ions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Enhancing Nanoparticle Detection in Interferometric Scattering (iSCAT) Microscopy Using a Mask R-CNN

Interferometric scattering microscopy (iSCAT) is a label-free optical microscopy technique that enables imaging of individual nano-objects such as nanoparticles, viruses, and proteins. Essential to this technique is the suppression of background scattering and identification of signals from nano-objects. In the presence of substrates with high roughness, scattering heterogeneities in the background, when coupled with tiny stage movements, cause features in the background to be manifested in background-suppressed iSCAT images. Traditional computer vision algorithms detect these background features as particles, limiting the accuracy of object detection in iSCAT experiments. Here, in this paper, we present a pathway to improve particle detection in such situations using supervised machine learning via a mask region-based convolutional neural network (mask R-CNN). Using a model iSCAT experiment of 19.2 nm gold nanoparticles adsorbing to a rough layer-by-layer polyelectrolyte film, we develop a method to generate labeled datasets using experimental background images and simulated particle signals and train the mask R-CNN using limited computational resources via transfer learning. We then compare the performance of the mask R-CNN trained with and without inclusion of experimental backgrounds in the dataset against that of a traditional computer vision object detection algorithm, Haar-like feature detection, by analyzing data from the model experiment. Results demonstrate that including representative backgrounds in training datasets improved the mask R-CNN in differentiating between background and particle signals and elevated performance by markedly reducing false positives. The methodology for creating a labeled dataset with representative experimental backgrounds and simulated signals facilitates the application of machine learning in iSCAT experiments with strong background scattering and thus provides a useful workflow for future researchers to improve their image processing capabilities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Thermal Transport and Mechanical Stress Mapping of a Compression Bonded GaN/Diamond Interface for Vertical Power Devices

Bonding diamond to the back side of gallium nitride (GaN) electronics has been shown to improve thermal management in lateral devices; however, engineering challenges remain with the bonding process and characterizing the bond quality for vertical device architectures. Here, in this study, integration of these two materials is achieved by room-temperature compression bonding centimeter-scale GaN and a diamond die via an intermetallic bonding layer of Ti/Au. Recent attempts at GaN/diamond bonding have utilized a modified surface activation bonding (SAB) method, which requires Ar fast atom bombardment immediately followed by bonding within the same tool under ultrahigh vacuum (UHV) conditions. The method presented here does not require a dedicated SAB tool yet still achieves bonding via a room-temperature metal–metal compression process. Imaging of the buried interface and the total bonding area is achieved via transmission electron microscopy (TEM) and confocal acoustic scanning microscopy (C-SAM), respectively. The thermal transport quality of the bond is extracted from spatially resolved frequency-domain thermoreflectance (FDTR) with the bonded areas boasting a thermal boundary conductance of >100 MW/m 2 ·K. Additionally, Raman maps of GaN near the GaN–diamond interface reveal a low level of compressive stress, <80 MPa, in well-bonded regions. FDTR and Raman were coutilized to map these buried interfaces and revealed some poor thermally bonded areas bordered by high-stress regions, highlighting the importance of spatial sampling for a complete picture of bond quality. Overall, this work demonstrates a novel method for thermal management in vertical GaN devices that maintains low intrinsic stresses while boasting high thermal boundary conductances.

Raman↗
DOE OSTI2019

High-Speed Quantification of Pore-Scale Multiphase Flow of Water and Supercritical CO 2 in 2-D Heterogeneous Porous Micromodels: Flow Regimes and Interface Dynamics

The pore-scale flow of CO 2 and water in 2-D heterogeneous porous micromodels over a Ca range of nearly three orders of magnitude was explored experimentally. The porous geometry is a close reprint of real sandstone, and the experiments were performed under reservoir-relevant conditions (i.e., 8 MPa and 21°C), thus ensuring relevance to practical CO 2 operations. High-speed fluorescent microscopy and image processing were employed to achieve temporally and spatially resolved data, providing a unique view of the dynamics underlying this multiphase flow scenario. Under conditions relevant to CO 2 sequestration, final CO 2 saturation was found to decrease and increase logarithmically with Ca within the capillary and viscous-fingering regimes, respectively, with a minimum occurring during regime crossover. Specific interfacial length generally scales linearly with CO 2 saturation, with higher slopes noted at high Ca due to stronger viscous and inertial forces, as supported by direct pore-scale observations. Additionally, statistical analysis of the interfacial movements revealed that pore-scale events are controlled by their intrinsic dynamics at low Ca, but overrun by the bulk flow at high Ca. During postfront flow, while permeability is typically correlated with total CO 2 saturation in the porous domain (regardless of its mobility), the saturation of active CO 2 pathways in the current study correlated very well with permeability. This alternate approach to characterize relative permeability could serve to mitigate hysteresis in relative permeability curves. Taken together, these results provide unique insights that address inconsistent observations in the literature and previously unanswered questions about the underlying flow dynamics of this important multiphase flow scenario.

42 ENGINEERING↗
DOE OSTI2022

Capillary Water in 2-D Drying-Cracking Soil Sub-Grain Models: Morphology and Kinematics of Evaporation and Haines Jumps

Morphing of capillary water during the drying of a cluster of three wet grains is imaged and measured. The uniqueness of the tests is in the grains being long cylinders to make the system as close to a 2-D one as possible. In this way, the Laplace pressure depends on the only one curvature of the meniscus, which can easily be followed and is continuously image processed. The motion of liquid/gas interface, and its rate, as well as of contact angle and perimeter, are also monitored. The drying water body has been known to undergo two modes of re-morphing: a slow, evaporation rate-controlled one and a fast, inertia-driven instabilities of the interfaces. Two particular forms of dynamic re-morphing are being followed: one, called classically an “air entry,” which is a meniscus jump before its approaching the throat between the top and a bottom grain and another jump of the bottom contact, with a splitting of the meniscus into two between only two of the three grains. Associated dynamic variables, capillary pressure, and surface tension forces developing prior to and in conjunction with the instabilities of the menisci are presented in a companion paper by Hueckel et al..

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Interpreting nanovoids in atom probe tomography data for accurate local compositional measurements

Quantifying chemical compositions around nanovoids is a fundamental task for research and development of various materials. Atom probe tomography (APT) and scanning transmission electron microscopy (STEM) are currently the most suitable tools because of their ability to probe materials at the nanoscale. Both techniques have limitations, particularly APT, because of insufficient understanding of void imaging. Here, we employ a correlative APT and STEM approach to investigate the APT imaging process and reveal that voids can lead to either an increase or a decrease in local atomic densities in the APT reconstruction. Simulated APT experiments demonstrate the local density variations near voids are controlled by the unique ring structures as voids open and the different evaporation fields of the surrounding atoms. We provide a general approach for quantifying chemical segregations near voids within an APT dataset, in which the composition can be directly determined with a higher accuracy than STEM-based techniques.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Differentiating multi-MeV, multi-ion spectra with CR-39 solid-state nuclear track detectors

The development of high intensity petawatt lasers has created new possibilities for ion acceleration and nuclear fusion using solid targets. In such laser-matter interaction, multiple ion species are accelerated with broad spectra up to hundreds of MeV. To measure ion yields and for species identification, CR-39 solid-state nuclear track detectors are frequently used. However, these detectors are limited in their applicability for multi-ion spectra differentiation as standard image recognition algorithms can lead to a misinterpretation of data, there is no unique relation between track diameter and particle energy, and there are overlapping pit diameter relationships for multiple particle species. In this report, we address these issues by first developing an algorithm to overcome user bias during image processing. Second, we use calibration of the detector response for protons, carbon and helium ions (alpha particles) from 0.1 to above 10 MeV and measurements of statistical energy loss fluctuations in a forward-fitting procedure utilizing multiple, differently filtered CR-39, altogether enabling high-sensitivity, multi-species particle spectroscopy. To validate this capability, we show that inferred CR-39 spectra match Thomson parabola ion spectrometer data from the same experiment. Filtered CR-39 spectrometers were used to detect, within a background of ~ 2 × 10 11 sr -1 J -1 protons and carbons, (1.3 ± 0.7) × 10 8 sr -1 J -1 alpha particles from laser-driven proton-boron fusion reactions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Global field reconstruction from sparse sensors with Veronoi tessellation-assisted deep learning

Achieving accurate and robust global situational awareness of a complex time-evolving field from a limited number of sensors has been a longstanding challenge. This reconstruction problem is especially difficult when sensors are sparsely positioned in a seemingly random or unorganized manner, which is often encountered in a range of scientific and engineering problems. Moreover, these sensors can be in motion and can become online or offline over time. The key leverage in addressing this scientific issue is the wealth of data accumulated from the sensors. As a solution to this problem, we propose a data-driven spatial field recovery technique founded on a structured grid-based deep-learning approach for arbitrary positioned sensors of any numbers. It should be noted that the naïve use of machine learning becomes prohibitively expensive for global field reconstruction and is furthermore not adaptable to an arbitrary number of sensors. In the present work, we consider the use of Voronoi tessellation to obtain a structured-grid representation from sensor locations enabling the computationally tractable use of convolutional neural networks. One of the central features of the present method is its compatibility with deep-learning based super-resolution reconstruction techniques for structured sensor data that are established for image processing. The proposed reconstruction technique is demonstrated for unsteady wake flow, geophysical data, and three-dimensional turbulence. The current framework is able to handle an arbitrary number of moving sensors, and thereby overcomes a major limitation with existing reconstruction methods. The presented technique opens a new pathway towards the practical use of neural networks for real-time global field estimation.

Fukami, Kai↗
DOE OSTI2018

Modeling the one-dimensional imager of neutrons (ODIN) for neutron response functions at the Sandia Z facility

The one-dimensional imager of neutrons (ODIN) at the Sandia Z facility consists of a 10-cm block of tungsten with rolled edges, creating a slit imager with slit widths of either 250, 500, or 750 μm. Designed with a 1-m neutron imaging line of sight, we achieve about 4:1 magnification and 500-μm axial spatial resolution. The baseline inertial confinement fusion concept at Sandia is magnetized liner inertial fusion, which nominally creates a 1-cm line source of neutrons. ODIN was designed to determine the size, shape, and location of the neutron producing region, furthering the understanding of compression quality along the cylindrical axis of magnetized liner implosions. Challenges include discriminating neutrons from hard x-rays and gammas with adequate signal-to-noise in the 2 × 10 12 deuterium-deuterium (DD) neutron yield range, as well as understanding the point spread function of the imager to various imaging detectors (namely, CR-39). Modeling efforts were conducted with MCNP6.1 to determine neutron response functions for varying configurations in a clean DD neutron environment (without x-rays or gammas). Configuration alterations that will be shown include rolled-edge slit orientation and slit width, affecting the resolution and response function. The experiment to determine CR-39 neutron sensitivity, with and without a high density polyethylene (n, p) converter, an edge spread function, and resolution will be discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗