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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 415 records · Page 23

Rapid macrovoid characterization in membranes prepared via nonsolvent-induced phase separation: A comparison between 2D and 3D techniques

Optimizing the performance of asymmetric membranes prepared via nonsolvent-induced phase separation (NIPS) requires a quantitative understanding of how processing variables influence membrane morphology. Presently, the most useful structural quantification techniques require 3D visualization of the membrane structure and are best suited for studies seeking detailed information on small datasets. This study proposes and validates a rapid and accurate technique for quantifying macroporosity (i.e., D m ), a simple descriptor of sublayer macrovoid content in asymmetric membranes D m . values measured from segmented cross-sectional imaging performed via X-ray computed tomography (XCT) and scanning electron microscopy (SEM) are presented and compared for three asymmetric membranes prepared from commercial polymers. Importantly, analyses of 3D XCT membrane reconstructions reveal that D m is described by a single, centralized mean, which demonstrates that macrovoid content is spatially homogenous. Thus, D m can be approximated from limited sampling of the 2D cross-sectional membrane structure via SEM. A proposed 2D SEM sampling method provides D m estimates within ±6% of corresponding 3D XCT values with 30 independent measurements for the three membranes. Further sensitivity is achieved using complementary descriptors such as macrovoid count density (i.e., C m ). This technique is thus a useful tool for characterizing macroporosity from a broad selection of membrane samples.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Development of a coupled experimental–computational approach for engineering optimization of spout-fluidized bed particle coating systems

The design of spout-fluidized bed (SFB) coating systems for nuclear particle fuels typically relies on trial-and-error processes, comprising iterative and time-consuming coating deposition experiments and post-deposition characterization. At an engineering scale, this approach to guided SFB system design is inefficient, highlighting the need for streamlined experimental methodologies which can correlate fluidization conditions to downstream coating outcomes. In this study, we combine time-resolved particle image velocimetry (PIV) with CFD–DEM simulations to benchmark hydrodynamic behavior in a 3D spout-fluidized bed. By exploiting easily accessible optical measurements of particle motion at the bed wall and within the spouting region, we obtain quantitative velocity fields that can be directly compared with model predictions of the occluded bed region, without resorting to complex imaging and characterization techniques such as X-ray or magnetic resonance tomography. Experimental benchmarking reveals strong agreement between CFD–DEM and PIV in the spout and annulus regions, while discrepancies near the wall highlight areas for future model development. Here, the proposed integrated experimental–numerical framework will enable a direct connection between measured variables and numerically predicted fluidization performance of dense, surrogate nuclear particle fuel feedstock such that experimental SFB component design can be rapidly evaluated, informing design decisions for nozzle geometry and operating conditions. Future work will extend this framework by correlating quantified fluidization metrics across nozzle geometries and operating conditions with the resulting coating morphology, microstructure, and uniformity. Establishing these correlations will enable predictive links between hydrodynamic performance and coating quality, providing a rational, scalable basis for optimizing SFB design prior to coating deposition.

CFD/DEM↗

Three-dimensional segmentation of luminal and adventitial borders in serial intravascular ultrasound images

Intravascular ultrasound (IVUS) provides exact anatomy of arteries, allowing accurate quantitative analysis. Automated segmentation of IVUS images is a prerequisite for routine quantitative analyses. We present a new three-dimensional (3D) segmentation technique, called active surface segmentation, which detects luminal and adventitial borders in IVUS pullback examinations of coronary arteries. The technique was validated against expert tracings by computing correlation coefficients (range 0.83-0.97) and William's index values (range 0.37-0.66). The technique was statistically accurate, robust to image artifacts, and capable of segmenting a large number of images rapidly. Active surface segmentation enabled geometrically accurate 3D reconstruction and visualization of coronary arteries and volumetric measurements.

NASA Discipline Cardiopulmonary↗

Range 7 Scanner Integration with PaR Robot Scanning System

An interface bracket and coordinate transformation matrices were designed to allow the Range 7 scanner to be mounted on the PaR Robot detector arm for scanning the heat shield or other object placed in the test cell. A process was designed for using Rapid Form XOR to stitch data from multiple scans together to provide an accurate 3D model of the object scanned. An accurate model was required for the design and verification of an existing heat shield. The large physical size and complex shape of the heat shield does not allow for direct measurement of certain features in relation to other features. Any imaging devices capable of imaging the entire heat shield in its entirety suffers a reduced resolution and cannot image sections that are blocked from view. Prior methods involved tools such as commercial measurement arms, taking images with cameras, then performing manual measurements. These prior methods were tedious and could not provide a 3D model of the object being scanned, and were typically limited to a few tens of measurement points at prominent locations. Integration of the scanner with the robot allows for large complex objects to be scanned at high resolution, and for 3D Computer Aided Design (CAD) models to be generated for verification of items to the original design, and to generate models of previously undocumented items. The main components are the mounting bracket for the scanner to the robot and the coordinate transformation matrices used for stitching the scanner data into a 3D model. The steps involve mounting the interface bracket to the robot's detector arm, mounting the scanner to the bracket, and then scanning sections of the object and recording the location of the tool tip (in this case the center of the scanner's focal point). A novel feature is the ability to stitch images together by coordinates instead of requiring each scan data set to have overlapping identifiable features. This setup allows models of complex objects to be developed even if the object is large and featureless, or has sections that don't have visibility to other parts of the object for use as a reference. In addition, millions of points can be used for creation of an accurate model [i.e. within 0.03 in. (=0.8 mm) over a span of 250 in. (=635 mm)].

Schuler, Jason↗

NASA Tech Briefs, August 2012

Topics covered include: Mars Science Laboratory Drill; Ultra-Compact Motor Controller; A Reversible Thermally Driven Pump for Use in a Sub-Kelvin Magnetic Refrigerator; Shape Memory Composite Hybrid Hinge; Binding Causes of Printed Wiring Assemblies with Card-Loks; Coring Sample Acquisition Tool; Joining and Assembly of Bulk Metallic Glass Composites Through Capacitive Discharge; 670-GHz Schottky Diode-Based Subharmonic Mixer with CPW Circuits and 70-GHz IF; Self-Nulling Lock-in Detection Electronics for Capacitance Probe Electrometer; Discontinuous Mode Power Supply; Optimal Dynamic Sub-Threshold Technique for Extreme Low Power Consumption for VLSI; Hardware for Accelerating N-Modular Redundant Systems for High-Reliability Computing; Blocking Filters with Enhanced Throughput for X-Ray Microcalorimetry; High-Thermal-Conductivity Fabrics; Imidazolium-Based Polymeric Materials as Alkaline Anion-Exchange Fuel Cell Membranes; Electrospun Nanofiber Coating of Fiber Materials: A Composite Toughening Approach; Experimental Modeling of Sterilization Effects for Atmospheric Entry Heating on Microorganisms; Saliva Preservative for Diagnostic Purposes; Hands-Free Transcranial Color Doppler Probe; Aerosol and Surface Parameter Retrievals for a Multi-Angle, Multiband Spectrometer LogScope; TraceContract; AIRS Maps from Space Processing Software; POSTMAN: Point of Sail Tacking for Maritime Autonomous Navigation; Space Operations Learning Center; OVERSMART Reporting Tool for Flow Computations Over Large Grid Systems; Large Eddy Simulation (LES) of Particle-Laden Temporal Mixing Layers; Projection of Stabilized Aerial Imagery Onto Digital Elevation Maps for Geo-Rectified and Jitter-Free Viewing; Iterative Transform Phase Diversity: An Image-Based Object and Wavefront Recovery; 3D Drop Size Distribution Extrapolation Algorithm Using a Single Disdrometer; Social Networking Adapted for Distributed Scientific Collaboration; General Methodology for Designing Spacecraft Trajectories; Hemispherical Field-of-View Above-Water Surface Imager for Submarines; and Quantum-Well Infrared Photodetector (QWIP) Focal Plane Assembly.

Source record↗

DeepPatent2: A Large-Scale Benchmarking Corpus for Technical Drawing Understanding

Abstract Recent advances in computer vision (CV) and natural language processing have been driven by exploiting big data on practical applications. However, these research fields are still limited by the sheer volume, versatility, and diversity of the available datasets. CV tasks, such as image captioning, which has primarily been carried out on natural images, still struggle to produce accurate and meaningful captions on sketched images often included in scientific and technical documents. The advancement of other tasks such as 3D reconstruction from 2D images requires larger datasets with multiple viewpoints. We introduce DeepPatent2, a large-scale dataset, providing more than 2.7 million technical drawings with 132,890 object names and 22,394 viewpoints extracted from 14 years of US design patent documents. We demonstrate the usefulness of DeepPatent2 with conceptual captioning. We further provide the potential usefulness of our dataset to facilitate other research areas such as 3D image reconstruction and image retrieval.

97 MATHEMATICS AND COMPUTING↗

Quick and Correlative TOF-SIMS Analysis of Dispersoid Content in Powder Feedstock and Printed Oxide Dispersion Strengthened Alloys

Oxide dispersion strengthening (ODS) of metal alloys, when combined with additive manufacturing (AM), can be used to solve high-temperature material application problems. The performance of ODS materials is dependent on nano-scale oxide being distributed uniformly throughout the metal matrix; a homogenous distribution is critical to the creep and yield strength. Therefore, quantifying and analyzing these oxide nanoparticles is essential for ODS alloy development. Assessing the chemistry and morphology of dispersoids in a material can be conducted using a variety of X-ray and electron-based methods. Time of Flight Secondary Ion Mass Spectrometry (TOF-SIMS), which assesses chemical concentrations from ion masses, provides the necessary spatial resolution and avoids potential spectral overlap of all elements present. In this work, preparing ODS powder feedstock and Laser Powder Bed Fusion AM was conducted with a NiCoCr alloy and yttria dispersoid, mixed with a Resodyn resonance mixer and printed on an EOS M100 3D printer at NASA Glenn. TOF-SIMS was performed on a Physical Electronics NanoTOF TRIFT V TOF-SIMS at Case Western Reserve University. The TOF-SIMS results collected on the feedstock powder samples correspond to the qualitative interpretation of the homogeneity of the oxide coating on the metal powder from SEM images. This provides quantitative support to the qualitative microscopy images collected earlier in the process design phase. The variance in the yttrium distribution on the powder feedstock samples also correlated with the variance of the yttrium distribution detected in the printed samples. This correlation supported the hypothesis that an even coating of yttria on the metal powders contributes to an even distribution of yttria in the printed material.

Laura G. Wilson↗

Beam Hardening Artifact Reduction in X-Ray CT Reconstruction of 3D Printed Metal Parts Leveraging Deep Learning and CAD Models

Nondestructive evaluation (NDE) of additively manufactured (AM) parts is important for understanding the impacts of various process parameters and qualifying the built part. X-ray computed tomography (XCT) has played a critical role in rapid NDE and characterization of AM parts. However, XCT of metal AM parts can be challenging because of artifacts produced by standard reconstruction algorithms as a result of a confounding effect called “beam hardening.” Beam hardening artifacts complicate the analysis of XCT images and adversely impact the process of detecting defects, such as pores and cracks, which is key to ensuring the quality of the parts being printed. In this work, we propose a novel framework based on using available computer-aided design (CAD) models for parts to be manufactured, accurate XCT simulations, and a deep-neural network to produce high-quality XCT reconstructions from data that are affected by noise and beam hardening. Using extensive experiments with simulated data sets, we demonstrate that our method can significantly improve the reconstruction quality, thereby enabling better detection of defects compared with the state of the art. We also present promising preliminary results of applying the deep networks trained using CAD models to experimental data obtained from XCT of an AM jet-engine turbine blade.

Ziabari, Amir↗

In Situ Nanoscale Dynamics Imaging in a Proton‐Conducting Solid Oxide for Protonic Ceramic Fuel Cells

Abstract Hydrogen fuel cells and electrolyzers operating below 600 °C, ideally below 400 °C, are essential components in the clean energy transition. Yttrium‐doped barium zirconate BaZr 0.8 Y 0.2 O 3‐d (BZY) has attracted a lot of attention as a proton‐conducting solid oxide for electrochemical devices due to its high chemical stability and proton conductivity in the desired temperature range. Grain interfaces and topological defects modulate bulk proton conductivity and hydration, especially at low temperatures. Therefore, understanding the nanoscale crystal structure dynamics in situ is crucial to achieving high proton transport, material stability, and extending the operating range of proton‐conducting solid oxides. Here, Bragg coherent X‐ray diffractive imaging is applied to investigate in situ and in 3D nanoscale dynamics in BZY during hydration over 40 h at 200 °C, in the low‐temperature range. An unexpected activity of topological defects and subsequent cracking is found on a nanoscale covered by the macroscale stability. The rearrangements in structure correlate with emergent regions of different lattice constants, suggesting heterogeneous hydration. The results highlight the extent and impact of nanoscale processes in proton‐conducting solid oxides, informing future development of low‐temperature protonic ceramic electrochemical cells.

25 ENERGY STORAGE↗

Lidar Mapping and Surveillance of Nuclear Infrastructure

Wear is unavoidable, whether it occurs in a nuclear facility or abridge on the highway its detection is crucial to preventing the failure of a system. While tools capable of detecting localized damage exist, industry lacks methods to evaluate the health of larger infrastructure. Routine inspection of nuclear facilities must be undertaken to ensure the safety of personnel and proper containment of nuclear materials. Most systems in a nuclear facility are designated as radioactive and confined spaces. To constantly monitor such systems in person would be hazardous but with a remote inspection tool it can be done in a safer and easier way. Lidar: Operates by emitting a laser pulse and looking for the reflected light. Allows user to gain 3D information of the environment. Project Objective: To develop an inspection tool for evaluating the health of large scale infrastructure using Lidar and machine learning. Autonomous Platform + Lidar + Data Fusion + Big Data. Use SLAM to build a 2D map of the environment. Point cloud created by transforming the points between reference frames. Point cloud to Surface: Inherent representation of surface geometry; Difficult to process with conventional techniques. Point cloud to Image: Easily processed by conventional machine learning algorithms. The quantity of data generated will necessitate a big data framework to manage the data for processing. Additional Sensors: Detecting the temperature (a) chemical makeup (b) and radiation emission (c) of a surface could assist in detecting problems. Human inspection can detect irregularities in both physical objects and their point-cloud representations. This is a good indication that mapping nuclear sites using Lidar will provide qualitative insight into the health of the facility. If deep learning can be used to automatically identify problem areas then the completed tool could become an invaluable asset.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Kinetics and evolution of solid-state metal dealloying in thin films with multimodal analysis

Thin-film solid-state metal dealloying (thin-film SSMD) is an emerging technique that uses self-organization to design nanostructured thin films. The resulting 3D bicontinuous nanostructures are promising for a wide range of applications, such as catalysis and energy storage. In this work, we prepared thin films by SSMD using Ti-Cu as the parent alloy and Mg as the solvent. Using a multimodal approach, we combined synchrotron X-ray spectroscopy, diffraction, and high-resolution electron-based spectroscopy and imaging to study their morphological, structural, and chemical evolution. The processing-structure relationship was analyzed as a function of parent alloy composition and dealloying temperature and time. Morphological transitions from globular, to lamellar, to bicontinuous structures, in conjunction with a ligament size evolution, were identified as functions of the parent alloy composition. The dealloying rate increased with increasing concentration of interdiffusing elements (dissolving component) in the parent alloy. The parting limit, a dealloying compositional threshold, was systematically analyzed and determined to be 30%–40%. The order of crystalline phase formation is CuMg 2 , Cu 2 Mg, and Ti; the Ti phase first shows self-reorganization during dealloying, separate from the crystallization process. The coarsening in thin-film SSMD was identified and not entirely self-similar; in addition to the increase of ligament size over time, the formation of larger globular ligaments were also observed. This work furthers our fundamental understanding of thin-film SSMD and nanostructured thin-film design, where the thermodynamic and kinetic effects differ from the bulk counterparts. The fact that dealloying and diffusion outpaces the crystallization and new phase formation also offers opportunities to utilize thin-film SSMD in certain alloy systems in which deleterious intermetallic phases need to be suppressed, that may not be possible in the bulk geometry.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Utilizing computer vision and artificial intelligence algorithms to predict and design the mechanical compression response of direct ink write 3D printed foam replacement structures

Additive Manufacturing (AM) of porous polymeric materials, such as foams, recently became a topic of intensive research due their unique combination of low density, impressive mechanical properties, and stress dissipation capabilities. Conventional methods for fabricating foams rely on complex and stochastic processes, making it challenging to achieve precise architectural control of structured porosity. In contrast, AM provides access to a wide range of printable materials, where precise spatial control over structured porosity can be modulated during the fabrication process enabling the production of foam replacement structures (FRS). Current approaches for designing FRS are based on intuitive understanding of their properties or an extensive number of finite element method (FEM) simulations. These approaches, however, are computationally expensive and time consuming. As such, in this work, we present a novel methodology for determining the mechanical compression response of direct ink write (DIW) 3D printed FRS using a simple cross-sectional image. By obtaining measurement data for a relatively small number of samples, an artificial neural network (ANN) was trained, and a computer vision algorithm was used to make inferences about foam compression characteristics from a single cross-sectional image. Finally, a genetic algorithm (GA) was used to solve the inverse design problem, generating the AM printing parameters that an engineer should use to achieve a desired compression response from a DIW printed FRS. The methods developed herein present an avenue for entirely autonomous design and analysis of additively manufactured structures using artificial intelligence.

36 MATERIALS SCIENCE↗

ThermalTracker 3D

ThermalTracker-3D is a stereo-vision solution for evaluating flight tracks of birds and bats around offshore wind turbines. Using a pair of thermal video cameras, the technology remotely senses movement of animals and objects, day and night, near critical assets. It generates motion tracks by collapsing a sequence of video frames from each camera into a single image that contains an entire flight track and then applies stereo-vision processing to transform the flight track into three dimensions. The approach allows tracking in near real time and automatically identifies moving objects based on features from the motion track and object size.

Matzner, Shari↗

Automated segmentation of porous thermal spray material CT scans with predictive uncertainty estimation

Abstract Thermal sprayed metal coatings are used in many industrial applications, and characterizing the structure and performance of these materials is vital to understanding their behavior in the field. X-ray computed tomography (CT) enables volumetric, nondestructive imaging of these materials, but precise segmentation of this grayscale image data into discrete material phases is necessary to calculate quantities of interest related to material structure. In this work, we present a methodology to automate the CT segmentation process as well as quantify uncertainty in segmentations via deep learning. Neural networks (NNs) have been shown to excel at segmentation tasks; however, memory constraints, class imbalance, and lack of sufficient training data often prohibit their deployment in high resolution volumetric domains. Our 3D convolutional NN implementation mitigates these challenges and accurately segments full resolution CT scans of thermal sprayed materials with maps of uncertainty that conservatively bound the predicted geometry. These bounds are propagated through calculations of material properties such as porosity that may provide an understanding of anticipated behavior in the field.

Martinez, Carianne↗

Visual Object Recognition and Tracking of Tools

A method has been created to automatically build an algorithm off-line, using computer-aided design (CAD) models, and to apply this at runtime. The object type is discriminated, and the position and orientation are identified. This system can work with a single image and can provide improved performance using multiple images provided from videos. The spatial processing unit uses three stages: (1) segmentation; (2) initial type, pose, and geometry (ITPG) estimation; and (3) refined type, pose, and geometry (RTPG) calculation. The image segmentation module files all the tools in an image and isolates them from the background. For this, the system uses edge-detection and thresholding to find the pixels that are part of a tool. After the pixels are identified, nearby pixels are grouped into blobs. These blobs represent the potential tools in the image and are the product of the segmentation algorithm. The second module uses matched filtering (or template matching). This approach is used for condensing synthetic images using an image subspace that captures key information. Three degrees of orientation, three degrees of position, and any number of degrees of freedom in geometry change are included. To do this, a template-matching framework is applied. This framework uses an off-line system for calculating template images, measurement images, and the measurements of the template images. These results are used online to match segmented tools against the templates. The final module is the RTPG processor. Its role is to find the exact states of the tools given initial conditions provided by the ITPG module. The requirement that the initial conditions exist allows this module to make use of a local search (whereas the ITPG module had global scope). To perform the local search, 3D model matching is used, where a synthetic image of the object is created and compared to the sensed data. The availability of low-cost PC graphics hardware allows rapid creation of synthetic images. In this approach, a function of orientation, distance, and articulation is defined as a metric on the difference between the captured image and a synthetic image with an object in the given orientation, distance, and articulation. The synthetic image is created using a model that is looked up in an object-model database. A composable software architecture is used for implementation. Video is first preprocessed to remove sensor anomalies (like dead pixels), and then is processed sequentially by a prioritized list of tracker-identifiers.

English, James↗

X-ray Nanocomputed Tomography in Zernike Phase Contrast for Studying 3D Morphology of Li–O 2 Battery Electrode

In this work, we investigated the lithium peroxide (Li 2 O 2 ) and pore size distribution in lithium-O 2 battery electrodes at different states of charge using transmission X-ray microscopy coupled with Zernike phase contrast to carry out nano-Computed Tomography. We report that such technique enables, at the nanoscale, to distinguish light elements such as carbon and Li 2 O 2 in Li-O 2 battery cathode electrodes. We verified by wave-propagation simulation that this approach efficiently improves the contrast of images in comparison with the pure absorption. The Li 2 O 2 distribution and thickness, interphases, and pore network are visualized and quantified, which give a valuable insight into our cathode architecture. From this 3D analysis, we highlight modifications of the air-cathode morphology and the Li 2 O 2 spatial organization as well as their potential implication in terms of carbon surface passivation and pore-clogging. After the full recharge process, this technique can also reveal the spatial distribution of the residual Li 2 O 2 and other byproducts.

25 ENERGY STORAGE↗

3D Reconstruction of a High-Energy Diffraction Microscopy Sample Using Multi-modal Serial Sectioning with High-Precision EBSD and Surface Profilometry

High-energy diffraction microscopy (HEDM) combined with in situ mechanical testing is a powerful nondestructive technique for tracking the evolving microstructure within polycrystalline materials during deformation. This technique relies on a sophisticated analysis of X-ray diffraction patterns to produce a three-dimensional reconstruction of grains and other microstructural features within the interrogated volume. However, it is known that HEDM can fail to identify certain microstructural features, particularly smaller grains or twinned regions. Characterization of the identical sample volume using high-resolution surface-specific techniques, particularly electron backscatter diffraction (EBSD), can not only provide additional microstructure information about the interrogated volume but also highlight opportunities for improvement of the HEDM reconstruction algorithms. In this study, a sample fabricated from undeformed “low solvus, high refractory” nickel-based superalloy was scanned using HEDM. The volume interrogated by HEDM was then carefully characterized using a combination of surface-specific techniques, including epi-illumination optical microscopy, zero-tilt secondary and backscattered electron imaging, scanning white light interferometry, and high-precision EBSD. Custom data fusion protocols were developed to integrate and align the microstructure maps captured by these surface-specific techniques and HEDM. The raw and processed data from HEDM and serial sectioning have been made available via the Materials Data Facility (MDF) at https://doi.org/10.18126/4y0p-v604 for further investigation.

36 MATERIALS SCIENCE↗

Three-dimensional X-ray diffraction imaging of dislocations in polycrystalline metals under tensile loading

Abstract The nucleation and propagation of dislocations is an ubiquitous process that accompanies the plastic deformation of materials. Consequently, following the first visualization of dislocations over 50 years ago with the advent of the first transmission electron microscopes, significant effort has been invested in tailoring material response through defect engineering and control. To accomplish this more effectively, the ability to identify and characterize defect structure and strain following external stimulus is vital. Here, using X-ray Bragg coherent diffraction imaging, we describe the first direct 3D X-ray imaging of the strain field surrounding a line defect within a grain of free-standing nanocrystalline material following tensile loading. By integrating the observed 3D structure into an atomistic model, we show that the measured strain field corresponds to a screw dislocation.

36 MATERIALS SCIENCE↗