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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 109 records · Page 6

Development of a Computer Tool to Analyze MISSE Images

The Materials International Space Station Experiment (MISSE) contains individual experiments focused on the investigation of the effects that occur because of the exposure of the material to the space environment. The specimens are mounted on the exterior of the International Space Station (ISS) on the MISSE-Flight Facility (MISSE-FF) and are exposed to the extreme environmental conditions. There have been 14 MISSE missions since the establishment in 2001. This project will evaluate MISSE 9 - MISSE 13 specimens and how they change due to the space environment.

MISSE↗

The investigation of the mechanical properties of thermoviscoelastic material

An image moire method is used to determine Poisson's ratio for PMMA at different temperatures. A uniaxial tensile specimen is strained axially to a constant strain, and the lateral strain and the axial strain are monitored by a period photographic record. Simultaneous recording of the tensile stress yields the Young's modulus in relaxation and the corresponding Poisson effect. The test temperatures range from room temperature to 125 C. The data are then used to deduce the bulk relaxation moduli.

Knauss, W. G.↗

Flaw Detection for Composite Materials Improved by Advanced Thermal Image Reconstruction Techniques

The development of advanced composite materials for use in space and propulsion components has seen considerable growth over the past few years. In addition to improvements that have been made in material properties and processing techniques, similar growth must be seen in the development of methods for the detection of flaws, either generated in service or during manufacturing. Thermal imaging techniques have proven to be successful for the nondestructive evaluation (NDE) of composite materials, but their detection capabilities decrease as flaw depth increases. The purpose of this research is to investigate advanced thermal imaging methods and thermal image processing technologies to increase the maximum depth below surface that a flaw can be detected and improve the contrast between flawed regions and sound regions.

Martin, Richard E.↗

An Evanescent Microwave Probe for Super-Resolution Nondestructive Imaging of Metals, Semiconductors, Dielectrics, Composites and Biological Specimens

Using evanescent microwaves with decay lengths determined by a combination of microwave wavelength (lambda) and waveguide termination geometry, we have imaged and mapped material non-uniformities and defects with a resolving capability of lambda/3800=79 microns at 1 GHz. In our method a microstrip quarter wavelength resonator was used to generate evanescent microwaves. We imaged materials with a wide range of conductivities. Carbon composites, dielectrics (Duroid, polymers), semiconductors (3C-SiC, polysilicon, natural diamond), metals (tungsten alloys, copper, zinc, steel), high-temperature superconductors, and botanical samples were scanned for defects, residual stresses, integrity of brazed junctions, subsurface features, areas of different film thickness and moisture content. The evanescent microwave probe is a versatile tool and it can be used to perform very fast, large scale mapping of a wide range of materials. This method of characterization compares favorably with ultrasound testing, which has a resolution of about 0.1 mm and suffers from high absorption in composite materials and poor transmission across boundaries. Eddy current methods which can have a resolution on the order of 50 microns are restricted to evaluating conducting materials. Evanescent microwave imaging, with careful choice of operating frequency and probe geometry, can have a resolution of up to 1 micron. In this method we can scan hot and moving objects, sample preparation is not required, testing is non-destructive, non-invasive and non-contact, and can be done in air, in liquid or in vacuum.

Pathak, P. S.↗

Neural Learning Based Blind Source Separation for Detection of Material Defects in Pulsed Thermography Images

In this paper, we introduce a neural learning-based approach to blind source separation for detection of material flaws in pulsed thermography (PT) images. This approach can be used to detect internal defects (pores) in metallic Additively Manufactured (AM) materials. Such defects occur in high-strength alloys produced with direct laser sintering AM method for nuclear energy applications. Pulsed thermal imaging system utilizes a high intensity flash lamp to rapidly heat surface of sample, a high sensitivity infrared camera to capture data of surface temperature variations. The data cube obtained with PT (stack of surface temperature images at different times) can be analyzed with image processing algorithms to detect material defects. Compared with conventional nondestructive evaluation (NDE) methods, such as digital radiography and ultrasonic testing, the PT pulsed infrared thermal detection technique has advantages of one-sided non-contact measurements, fast processing of large sample areas captured in one image. Detection of small material defects requires finding features in thermal images, which have signal contrast levels approaching sensitivity limit of IR camera. In this study, an optimized Neural Learning based Blind Source Separation (NLBSS) algorithm, including Principal Component Analysis (PCA), and Independent Component Analysis (ICA) is demonstrated to automatically extract principal temporal and spatial features of thermography frames to enhance flaw detection. By using the NLBSS algorithm, material internal defects can be automatically detected. Furthermore, this processing approach compensates for experimental thermal imaging artifacts, such as noise and uneven heating. By merging artificial intelligence with the photothermic, the NDE system detects internal calibrated defects of various sizes and depths in AM nuclear-grade metallic alloys.

Zhang, Xin↗

Characteristics of a 60-Inch Arc-Image Furnace and Application to the Study of Materials

An arc-image furnace consisting of two paraboloidal searchlight mirrors 60 inches (152 centimeters) in diameter and a modified electrode mechanism has been developed and has been used as an operational piece of laboratory equipment for the study of the elevated-temperature behavior of engineering materials. Using two types of 16-millimeter high-current anodes and operating in the atmosphere at current levels of 80 to 550 amperes, the arc-image furnace provides a heating-rate range from 100 to 1,000 Btu per square foot-sec (27 to 270 cal per centimeters squared-sec) with good reliability and reproducibility. Running times vary from 60 seconds at 550 amperes to 10 minute or longer at the lower current levels.

Peters, Roger W.↗

Optimizing Sub-Sampled STEM Imaging for Beam Sensitive Materials and Dynamic Processes

The goal of this research is to quantify how sub-sampling and inpainting (broadly classified as methods in Artificial Intelligence) can be used to obtain the highest resolution images under any experimental conditions, and to use this to reproducibly control dynamic in-situ processes in gases and liquids on the nanoscale. As atomic diffusion under non-extreme conditions is typically in the sub-ms time domain, the ability to use this sub-sampling approach to reduce the effect of the electron beam and to speed-up the imaging process represents the optimum combination of spatial and temporal resolution achievable in a conventional microscope. This LDRD funding demonstrated the feasibility of imaging dynamics in complex energy systems using this methodology by establishing the optimum experimental sampling conditions (optimizing the number of pixels, speed, contrast and resolution) for each experiment. For in-situ liquid cell experiments this analysis shows that simply by distributing the same dose and dose rate maximally in space and time, the kinetics for radiolysis products can be modified significantly. The final stage of this work, i.e. the experimental demonstration of its use for nucleation and growth, was severely curtailed by issues with COVID19. However, all the experiments are designed and currently underway, with the goal to complete the final demonstration and publication as soon as safety concerns for laboratory access are met.

36 MATERIALS SCIENCE↗

New perspectives on materials and device dynamics using time-resolved full-field diffraction X-ray imaging

Understanding how materials evolve during synthesis, processing, or device operation requires experimental access to structural dynamics across wide ranges of length and time scales, often in bulk samples or even within packaged devices. Time-resolved full-field diffraction X-ray microscopy has recently emerged as a powerful way to meet this need by combining the penetration and structural sensitivity of X-ray diffraction with objective-lens-based magnification, sensitive high-resolution X-ray imaging detectors, and pump-probe and real-time imaging strategies. Advances in full-field X-ray diffraction microscopy, often termed dark-field X-ray microscopy (DFXM), enable simultaneous imaging of extended fields of view while retaining crystallographic selectivity. Time-resolved DFXM promises to enable advances in materials processing and dynamics, electrochemical and photochemical processes, and the design of electronic devices. This Perspective summarizes the key instrumental concepts that define the performance of these methods, including the choice of imaging optics, detector considerations, and the impact of source time structure at synchrotron light sources and X-ray free-electron lasers (XFELs). We discuss the simultaneous use of complementary imaging modes that are increasingly used in practice. The early demonstrations of the potential of time-resolved DFXM include real-time defect and grain-boundary dynamics during metal annealing, stroboscopic imaging of functional devices with high strain sensitivity, and ultrafast pump-probe implementations at XFELs that directly visualize acoustic-wave propagation and energy dissipation in bulk crystals.

Dark-field X-ray microscopy↗

Height Control and Deposition Measurement for the Electron Beam Free Form Fabrication (EBF3) Process

A method of controlling a height of an electron beam gun and wire feeder during an electron freeform fabrication process includes utilizing a camera to generate an image of the molten pool of material. The image generated by the camera is utilized to determine a measured height of the electron beam gun relative to the surface of the molten pool. The method further includes ensuring that the measured height is within the range of acceptable heights of the electron beam gun relative to the surface of the molten pool. The present invention also provides for measuring a height of a solid metal deposit formed upon cooling of a molten pool. The height of a single point can be measured, or a plurality of points can be measured to provide 2D or 3D surface height measurements.

Seufzer, William J.↗

Imaging of dynamic processes in materials with a laser-wakefield accelerator

Betatron x rays generated from laser-wakefield accelerators are a promising source for imaging dynamic processes in materials. Here, we present proof-of-concept imaging of microstructural evolution in a hypermonotectic Al-Bi alloy, which consists of liquid Bi particles in a solid Al matrix. The images capture the elongation and fragmentation of Bi particles upon isothermal annealing. Because of the femtosecond time scale of the betatron source, the images are not subject to motion blur, whereas the accessibility of the source allows for studies of long-term processes such as annealing. The high-resolution data reveal that the evolution of Bi particles is mediated by an interplay of grain-boundary wetting and morphological instability, in stark contrast to an earlier proposal for (inverse) coarsening.

Alloys↗

Development and Operation of a Material Identification and Discrimination Imaging Spectroradiometer

Many imaging applications require quantitative determination of a scene's spectral radiance. This paper describes a new system capable of real-time spectroradiometric imagery. Operating at a full-spectrum update rate of 30Hz, this imager is capable of collecting a 30 point spectrum from each of three imaging heads: the first operates from 400 nm to 950 nm, with a 2% bandwidth; the second operates from 1.5 micro-m to 5.5 micro-m with a 1.5% bandwidth; the third operates from 5 micro-m to 12 micro-m, also at a 1.5% bandwidth. Standard image format is 256 x 256, with 512 x 512 possible in the VIS/NIR head. Spectra of up to 256 points are available at proportionately lower frame rates. In order to make such a tremendous amount of data more manageable, internal processing electronics perform four important operations on the spectral imagery data in real-time. First, all data in the spatial/spectral cube of data is spectro-radiometrically calibrated as it is collected. Second, to allow the imager to simulate sensors with arbitrary spectral response, any set of three spectral response functions may be loaded into the imager including delta functions to allow single wavelength viewing; the instrument then evaluates the integral of the product of the scene spectral radiances and the response function. Third, more powerful exploitation of the gathered spectral radiances can be effected by application of various spectral-matched filtering algorithms to identify pixels whose relative spectral radiance distribution matches a sought-after spectral radiance distribution, allowing materials-based identification and discrimination. Fourth, the instrument allows determination of spectral reflectance, surface temperature, and spectral emissivity, also in real-time. The spectral imaging technique used in the instrument allows tailoring of the frame rate and/or the spectral bandwidth to suit the scene radiance levels, i.e., frame rate can be reduced, or bandwidth increased to improve SNR when viewing low radiance scenes. The unique challenges of design and calibration are described. Pixel readout rates of 160 MHz, for full frame readout rates of 1000 Hz (512 x 512 image) present the first challenge; processing rates of nearly 600 million integer operations per second for sensor emulation, or over 2 billion per second for matched filtering, present the second. Spatial and spectral calibration of 66,536 pixels (262,144 for the 512 x 512 version) and up to 1,000 spectral positions mandate novel decoupling methods to keep the required calibration memory to a reasonable size. Large radiometric dynamic range also requires care to maintain precision operation with minimum memory size.

Dombrowski, Mark↗

Automated Scoring of Morphological Changes in Images of Pentaerythritol Tetranitrate

Recent advances in characterization techniques that generate large datasets of material microstructure images require robust, automated image-processing. We applied an unsupervised anomaly detection method called feature anomaly detection system (FADS) to automatically detect and quantify microstructure changes in images of the explosive pentaerythritol tetranitrate (PETN) aged at various temperatures. We demonstrated the FADS approach on two-dimensional images extracted from computed tomography scans, but the same technique can be readily applied to other imaging modalities. FADS calculates anomaly scores on the basis of differences in filter activations of nominal and test data in pretrained convolutional neural networks. The FADS scores successfully differentiated between pristine PETN and PETN aged at a temperature where material coarsening occurred. Morphological metric analysis of segmented images verified observed trends in FADS scores as a function of aging temperature and aging time, specifically by calculating volume fractions, specific boundary lengths, two-point correlation functions, and local thicknesses. Here, the FADS technique has two important advantages compared to traditional morphological analysis: First, it uses grayscale images as input, rather than images that are segmented to separate the appropriate phases; and second, FADS scores capture any type of changes among image sets, rather than requiring prior knowledge or selection of a relevant set of metrics.

Accelerated aging↗

Invariant discovery of features across multiple length scales: Applications in microscopy and autonomous materials characterization

Physical imaging is a foundational characterization method in areas from condensed matter physics and chemistry to astronomy and spans length scales from atomic to universe. Images encapsulate crucial data regarding atomic bonding, materials microstructures, and dynamic phenomena such as microstructural evolution and turbulence, among other phenomena. The challenge lies in effectively extracting and interpreting this information. Variational Autoencoders (VAEs) have emerged as powerful tools for identifying the underlying factors of variation in image data, providing a systematic approach to distilling meaningful patterns from complex data sets. However, a significant hurdle in their application is the definition and selection of appropriate descriptors reflecting local structures. Here, we introduce the scale-invariant VAE approach (SI-VAE) based on the progressive training of the VAE with the descriptors sampled at different length scales. The SI-VAE allows the discovery of the length scale-dependent factors of variation in the system. Here, we illustrate this approach using the ferroelectric domain images and generalize it to the movies of the electron-beam induced phenomena in graphene and topography evolution across combinatorial libraries. This approach can further be used to initialize the decision making in automated experiments including structure–property discovery and can be applied across a broad range of imaging methods. This approach is universal and can be applied to any spatially resolved data including both experimental imaging studies and simulations, and can be particularly useful for exploration of phenomena such as turbulence and scale-invariant transformation fronts.

36 MATERIALS SCIENCE↗

Hyperspectral imaging for real-time waste materials characterization and recovery using endmember extraction and abundance detection

Hyperspectral imaging, combined with advanced spectral unmixing techniques and artificial intelligence, offers a powerful solution for improving material identification and classification. Here, this study evaluates the effectiveness of the pixel purity index and the sequential maximum angle convex cone algorithms in extracting and validating spectral signatures from pure samples of paper components (cellulose and lignin) and plastic (polypropylene). Principal-component analysis showed that both algorithms captured nearly all relevant variance for the tested materials. Spectral signatures were compared using the spectral angle mapper, revealing high similarity in the short-wave infrared region and greater variability in the visible near-infrared range. The methodology was then applied to a disposable coffee cup to detect and quantify mixed materials, accurately estimating material abundance and object area with less than 1% error. This approach enhances material classification, supporting product verification, quality control, and automated sorting for sustainable waste management and resource recovery.

36 MATERIALS SCIENCE↗

Phase Change Material for Temperature Control of Imager or Sounder on GOES Type Satellites in GEO

This paper uses phase change material (PCM) in the scan cavity of an imager or sounder on satellites in geostationary orbit (GEO) to maintain the telescope temperature stable. When sunlight enters the scan aperture, solar heating causes the PCM to melt. When sunlight stops entering the scan aperture, the PCM releases the thermal energy stored to keep the components in the telescope warm. It has no moving parts or bimetallic springs. It reduces heater power required to make up the heat lost by radiation to space through the aperture. It is an attractive thermal control option to a radiator with a louver and a sunshade.

phase change material↗

Evaluation of lunar rocks and soils for resource utilization: Detailed image analysis of raw materials and beneficiated products

The rocks and soils of the Moon will be the raw materials for fuels and construction needs at a lunar base. This includes sources of materials for the generation of hydrogen, oxygen, metals, and other potential construction materials. For most of the bulk material needs, the regolith, and its less than 1 cm fraction, the soil, will suffice. But for specific mineral resources, it may be necessary to concentrate minerals from rocks or soils, and it is not always obvious which is the more appropriate feedstock. Besides an appreciation of site geology, the mineralogy and petrography of local rocks and soils is important for consideration of the resources which can provide feedstocks of ilmenite, glass, agglutinates, anorthite, etc. In such studies, it is very time-consuming and practically impossible to correlate particle counts (the traditional method of characterizing lunar soil petrography) with accurate modal analyses and with mineral associations in multi-mineralic grains. But x ray digital imaging, using x rays characteristic of each element, makes all this possible and much more (e.g., size and shape analysis). An application of beneficiation image analysis, in use in our lab (Oxford Instr. EDS and Cameca SX-50 EMP), was demonstrated to study mineral liberation from lunar rocks and soils. Results of x ray image analysis are presented.

Taylor, Lawrence A.↗

MatPhase: Material phase prediction for Li-ion Battery Reconstruction using Hierarchical Curriculum Learning

Li-ion Batteries (LIB), one of the most efficient energy storage devices, are used extensively in many industrial applications. These batteries consist of electrodes that are put together with heterogeneous material compositions. Imaging data of these battery electrodes obtained from X-ray tomography can explain the distribution of material constituents and allow reconstructions to study electron transport pathways. Such reconstructions of material constituents help quantify various associated properties of electrodes (e.g., volume-specific surface area, porosity) which determine the performance of batteries. These images often suffer from low image contrast between multiple material constituents, hence making it difficult for humans to distinguish and characterize these constituents through visual inspection. A minor error in detecting distributions of the material constituents can lead to magnified errors in the calculated parameters of material properties (e.g., porosity). We present MatPhase, a novel hierarchical curriculum learning technique to address the complex task of estimating material constituent distribution in battery electrodes. MatPhase comprises three modules: (i) an uncertainty-aware global model trained to yield inferences conditioned upon global knowledge of material distribution, (ii) a local model to capture relatively more fine-grained (local) distributional signals, (iii) an aggregator model to appropriately fuse the local and global effects towards obtaining the final distribution. On average, MatPhase improves prediction up to 8.5% relative to other sophisticated modeling pipelines and state-of-the-arts (SOTA) object detection models employed in the performance comparison.

Tabassum, Anika↗