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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Microstructural characterization of the oxidation of nuclear graphite under chronic and accident conditions via XCT and synchrotron XCT

Graphite is a candidate material to moderate fast neutrons and for structural components in the US next-generation graphite-moderated reactors. A graphite core is conceived as a large formation of interconnected bricks that primarily serves as the moderator of fast neutrons, holds instrumentation, fuel elements, control rods, and is a receptacle for molten salts for Molten Salt Reactors (MSRs) designs. During the operation of a graphite-moderated power plant, graphite components might be subjected to chronic oxidation during normal operating conditions or aggressive oxidation as a result of accidental ingress of air that reacts vigorously with the graphite core. Using synchrotron, x-ray computed tomography (XCT), this research systematically characterized microstructural changes that accompanied these two oxidation scenarios. Chronic oxidation was studied by characterizing IG-110, PCEA, and NBG-18 specimens that were gradually oxidized in air at a low temperature (520°C). The accidental ingress of air into the graphite reactor was simulated by subjecting the grades mentioned above to high-temperature oxidation at approximately 750°C. This research is the first, in situ, systematic characterization of nuclear graphite microstructural evolution that can be associated with the two possible oxidation scenarios and provide insight into related repercussions. The results show that the microstructure and pore connectivity influence the rate of oxidation and evolution of the microstructure under the two oxidation regimes. These results are crucial to understanding which grades are more resilient to each type of oxidation and documenting the damage created in the graphite.

Arregui Mena, Jose'↗
NASA NTRS1997

Microstructure Characterization and Stress Corrosion Evaluation of Autogenous and Hybrid Friction Stir Welded Al-Cu-Li 2195 Alloy

Friction stir welding process is being evaluated for application on the Al-Cu-Li 2195 Super-Light Weight External Tank of the Space Transportation System. In the present investigation Al-Cu-Li 2195 plates were joined by autogenous friction stir welding (FSW) and hybrid FSW (friction stir welding over existing variable polarity plasma arc weld). Optical microscopy and transmission electron microscopy (TEM) were utilized to characterize microstructures of the weldments processed by both welding methods. TEM observations of autogenous FSW coupons in the center section of the dynamically-recrystallized zone showed an equiaxed recrystallized microstructure with an average grain size of approx. 3.8 microns. No T(sub 1), precipitates were present in the above-mentioned zone. Instead, T(sub B) and alpha precipitates were found in this zone with a lower population. Alternate immersion, anodic polarization, constant load, and slow strain tests were carried out to evaluate the general corrosion and stress-corrosion properties of autogenous and hybrid FSW prepared coupons. The experimental results will be discussed.

Li, Zhixian↗
DOE OSTI2020

Microstructural characterization of dendritic evolution using two-point statistics

Characterizing large four-dimensional materials datasets is difficult due to the presence of complex mi- crostructures and time-varying length scales. We showcase the use of two-point statistics as an efficient and un-biased way of extracting materials parameters from an Al-Cu alloy during solidification. The evo- lution of dendrite primary arm thickness, average secondary arm spacing, and average tip-to-tip spacing were tracked using two-point Pearson auto-correlations of scaled mean curvatures. Insights into compet- itive side-branching are also reported. We show both visually and quantitatively that most length scales change rapidly during early stages of dendritic growth, but slow as diffusion fields of dendrites overlap.

Aluminum alloys↗
DOE OSTI2021

Quantitative Microstructural Characterization of Plutonium Oxalate Auto-Degradation and Evidence for PuO2 Nanocrystal Formation

It has been known since the 1950s that plutonium oxalate powders change color and lose mass over time when stored at room temperature in air. Despite several studies monitoring these changes, there are still discrepancies in the literature regarding the speciation of intermediate and final products that result from this decomposition. Presented here for the first time is a comprehensive series of time-resolved powder X-ray diffraction experiments coupled with solid-state optical spectroscopy and electron microscopy of aged plutonium (III) and (IV) oxalate powders. These data provide fresh insight into the chemical and structural changes that occur in these solids over time at room temperature and represent new evidence suggesting both plutonium (III) and plutonium (IV) oxalates decompose to form nanocrystalline plutonium oxide in the solid state.

Corbey, Jordan F.↗
DOE OSTI2020

Microstructural characterization of cold-worked 316 stainless steel flux thimble tubes irradiated up to 100 dpa in a commercial Pressurized Water Reactor

Two flux thimble tubes (FTT) made of 15% cold-worked 316 stainless steel (SS) were harvested from Ringhals Pressurized Water Reactor (PWR) Unit 2, with peak damages of 76 and 100 displacements per atom (dpa) after 29 and 34 years’ service, respectively. Specimens sectioned from parent tubes were comprehensively characterized with nominal damage levels of ~0, ~41, ~74, 76, and 100 dpa at a nominal temperature range of 285-323 °C. Both FTTs contained helium and hydrogen gases as transmutation products. The helium follows a production rate of ~9.8 appm/dpa, while environmental factors complicate hydrogen production obscuring an exact H/dpa ratio. Irradiation-induced dislocation loops, nano-cavities, solute clusters, and microsegregation were all observed. The dislocation loops and nano-cavities indicated saturation at 41 dpa. The solute clusters continued to evolve with Ni-Si clusters formed at 41 dpa, and Ni-Si-Mn-P clusters formed at 74 and 100 dpa, but neither clusters exhibited distinct diffraction patterns at any damage levels. Solute clusters were observed to frequently be co-located with dislocation loops, but fully decorated loops were rarely detected. Significant radiation-induced segregation (RIS) was observed around grain boundaries at all damage levels. The modified inverse Kirkendall (MIK) model captured the RIS behavior of major elements. Large cavities within or around an Mn-S rich region were observed for the first time. Through all the damage levels, void swelling is always below 0.05%, making significant dimensional change unlikely in core internals when used at similar conditions. Meanwhile, the role of overwhelming nanocavities, presumably helium bubbles, should be considered in other potential degradation mechanisms, including irradiation-assisted stress corrosion cracking, embrittlement, and loss of fracture toughness, which remain the concerns for extended operation of nuclear power plants.

Post Irradiation Examination, Isotope Dilution Mas↗
DOE OSTI2020

Microcomputed X-Ray Tomographic Imaging and Image Processing for Microstructural Characterization of Explosives

Microstructural characterization of composite high explosives (HEs) has become increasingly important over the last several decades in association with the development of high fidelity mesoscale modeling and an improved understanding of ignition and detonation processes. HE microstructure influences not only typical material properties (e.g., thermal, mechanical) but also reactive behavior (e.g., shock sensitivity, detonation wave shape). A detailed nondestructive 3D examination of the microstructure has generally been limited to custom-engineered samples or surrogates due to poor contrast between the composite constituents. Highly loaded (>90 wt%) HE composites such as plastic-bonded explosives (PBX) are especially difficult. Here, we present efforts to improve measurement quality by using single and dual-energy microcomputed X-ray tomography and state-of-the-art image processing techniques to study a broad set of HE materials. Some materials, such as PBX 9502, exhibit suitable contrast and resolution for an automatic segmentation of the HE from the polymer binder and the voids. Other composite HEs had varying levels of success in segmentation. Post-processing techniques that used commercially available algorithms to improve the segmentation quality of PBX 9501 as well as zero-density defects such as cracks and voids could be easily segmented for all samples. Aspects of the materials that lend themselves well to this type of measurement are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Deep Neural Network Algorithm for CMC Microstructure Characterization and Variability Quantification

Microstructure characterization and variability quantification are crucial for understanding ceramic matrix composites (CMCs) mechanical behavior and deformation mechanisms across length scales. Traditionally, analyses of the micrographs obtained from microscopy are labor-intensive. However, with the vast improvement in computer vision (CV) and deep learning (DL), an automated algorithm can be designed to extract essential microstructure variability from micrographs which can then be used to construct a statistically representative volume element (SRVE). The DL-based algorithm spans the taxonomy of microstructure analyses, including semantic segmentation of microstructure constituents, secondary phases, matrix/fiber interface, and defects, and quantifying the microstructure variability in terms of probability distributions. In this work, C/SiNC and SiC/SiNC CMCs microstructures are semantically segmented through a deep convolutional neural network, followed by variability quantification through the implementation of a fully connected regression layer, hence forming a deep regression network. The deep regression network operates in a feedforward regime, in which the neuron output signal traverses through the network in a unidirectional manner. The weight tensor associated with each layer is updated through a backpropagation stochastic gradient descent approach. The input gray-scale image obtained through in-house scanning electron microscope and confocal microscope micrographs is augmented through affine transformations to increase the training set size, which is then processed through four strided convolutional layers. This compresses the image resolution by half at each layer while increasing the image depth by applying different filters (image encoding). The class activation maps (CAMs) corresponding to the applied filters highlight the key architectural features and assist with the semantic segmentation of the microstructure.

Hamza, Mohamed H.↗
DOE OSTI2023

A data-driven framework for permeability prediction of natural porous rocks via microstructural characterization and pore-scale simulation

Understanding the microstructure–property relationships of porous media is of great practical significance, based on which macroscopic physical properties can be directly derived from measurable microstructural informatics. However, establishing reliable microstructure–property mappings in an explicit manner is difficult, due to the intricacy, stochasticity, and heterogeneity of porous microstructures. In this paper, a data-driven computational framework is presented to investigate the inherent microstructure–permeability linkage for natural porous rocks, where multiple techniques are integrated together, including microscopy imaging, stochastic reconstruction, microstructural characterization, pore-scale simulation, feature selection, and data-driven modeling. A large number of 3D digital rocks with a wide porosity range are acquired from microscopy imaging and stochastic reconstruction techniques. A broad variety of morphological descriptors are used to quantitatively characterize pore microstructures from different perspectives, and they compose the raw feature pool for feature selection. Here high-fidelity lattice Boltzmann simulations are conducted to resolve fluid flow passing through porous media, from which reliable permeability references are obtained. The optimal feature set that best represents permeability is identified through a performance-oriented feature selection process, upon which a cost-effective surrogate model is rapidly fitted to approximate the microstructure-permeability mapping via data-driven modeling. This surrogate model exhibits great advantages over empirical/analytical formulas in terms of prediction accuracy and generalization capacity, which can predict reliable permeability values spanning four orders of magnitude. Besides, feature selection also greatly enhances the interpretability of the data-driven prediction model, from which new insights into the mechanism of how microstructural characteristics determine intrinsic permeability are obtained.

58 GEOSCIENCES↗
NASA NTRS1995

Microstructural Characterization of Reaction-Formed Silicon Carbide Ceramics

Microstructural characterization of two reaction-formed silicon carbide ceramics has been carried out by interference layering, plasma etching, and microscopy. These specimens contained free silicon and niobium disilicide as minor phases with silicon carbide as the major phase. In conventionally prepared samples, the niobium disilicide cannot be distinguished from silicon in optical micrographs. After interference layering, all phases are clearly distinguishable. Back scattered electron (BSE) imaging and energy dispersive spectrometry (EDS) confirmed the results obtained by interference layering. Plasma etching with CF4 plus 4% O2 selectively attacks silicon in these specimens. It is demonstrated that interference layering and plasma etching are very useful techniques in the phase identification and microstructural characterization of multiphase ceramic materials.

Singh, M.↗
DOE OSTI2022

Three-dimensional microstructural characterization of FBR MOX fuel and the contribution of microstructural features to the thermal conductivity of the fuel

Combination of microstructural characterization, property measurements, and phase field modeling is used to investigate fast breeder reactor (FBR) mixed oxide (MOX) fuel irradiated to burnup of 13.7% fissions per initial metal atom (FIMA). Here, the fuel was characterized at different radial locations, which revealed that grey phase can be present in the central region if it nucleates on five metal precipitates (FMPs). In addition, in the mid-radial region FMPs do not diffuse out of the region once formed and the size of Pd–Te precipitates is dictated by the porosity present in the region. Thermal conductivity measurements were conducted as a function of radial location and the microstructure of the fuel was correlated with the observed trend. Reconstructions of the 3D solid and gaseous fission product structures in different regions of the fuel were used to simulate the effective thermal conductivity (ETC) of the respective regions and determine which microstructural feature has the strongest impact on thermal conductivity. Based on conducted assessment, FMPs and Pd-Te precipitates improve local conductivity of central and mid-radial regions even in the presence of grey phase, but defects are primary contributor to the degradation of thermal conductivity on the periphery of the fuel.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

3-D reconstruction and microstructural characterization of neutron-irradiated U-10Zr fuel using FIB-SEM serial sectioning

Herein, focused ion beam-scanning electron microscopy serial sectioning was applied to characterize the three-dimensional (3-D) porosity and phase regions of a neutron-irradiated U-10 wt% Zr fuel. The specimen was removed from an intermediate radial region of a fuel pin irradiated to 5.7 at.% burn-up. Backscattered electron imaging and energy-dispersive spectroscopy were performed on each serial section, allowing for the characterization of microstructural morphology and composition. Porosity size followed a lognormal distribution, ranging from 1.46 × 10 –4 to 25.58 µm 3 with a total porosity volume fraction of 13.02%. Distinctive microstructural regions were identified by composition and porosity: (1) a Zr-rich region with an average composition of 28.4 wt% Zr and a local porosity fraction of 6.88%, and (2) a U-rich region with an average composition of 97.0 wt% U and a local porosity fraction of 14.11%; subdivided into U-rich—high porosity (16.68%) and U-rich—low porosity (8.04%) regions. The detailed 3-D compositional and porosity regions can improve nuclear fuel performance codes.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Adoption of image-driven machine learning for microstructure characterization and materials design: A Perspective

Microstructure characterization enables the development of structure-processing-property relationships critical to several research areas within the broad field of materials science, from alloy design to the assessment of corrosion resistance, and failure analysis. Conventional approaches to material characterization have relied on either qualitative inference by the human ex-pert or software applications that can extract high-level features from images, such as boundary segmentation, average grain diameter, etc. Such approaches rely heavily on subject matter expert user intervention and knowledge of what phases or more generally, what microstructural features, are of interest. The recent surge in the adoption of machine learning techniques to address problems in materials engineering has brought with it an increased interest and application of Image Driven Machine Learning (IDML) approaches. In this work, we review the applications of IDML to the field of materials characterization. A canonical hierarchy of stages is defined, which when put sequentially together completes an IDML study: problem definition, dataset building, model selection and training, model evaluation, and integration with existing instrumentation or simulation workflow. The studies reviewed in this work are analyzed from the perspective of each of these stages. Such a review permits agranular assessment of the field, for example the impact of IDML on materials characterization at the nanoscale, the size of a typical dataset required to train a semantic segmentation model on electron microscopy images, ubiquitousness of transfer learning in the domain, etc. Finally, we discuss the importance of interpretability and explainability in the field of IDML for materials characterization, and provide an overview of two emerging techniques in the field: semantic segmentation and generative adversarial networks.

Baskaran, Arun↗
DOE OSTI2022

In Situ Microstructure Characterization of Potassium Di-Phosphate (KDP) Densification during Cold Sintering

In order for ceramic additive manufacturing (AM) to achieve its full potential, it is increasingly important to develop a more rigorous understanding of fundamental phenomena that govern the kinetics and thermodynamics of ceramic AM processes. In the case of additive build processes, such as direct ink write and ceramic extrusion, methods for densifying the resulting green-body product need to be considered to complement the efficiencies of ceramics AM, itself. One densification route, at least for monolithic components, built layer-by-layer, is offered by the recently developed cold sintering process, whereby high-density final product is achieved through addition of a small amount of liquid solvent and application of modest uniaxial compressive stress at relatively low temperature. In situ small-angle X-ray scattering methods and X-ray diffraction have been applied to characterize and quantify the pore morphology evolution during cold sintering for a model system: potassium di-phosphate, KH 2 PO 4 (KDP). It is shown that both temperature and applied stress affect the densification rate, but stress has a stronger effect on the evolving morphology. A regime with an approximate linear densification rate can be identified, yielding an effective densification activation energy of ≈90 kJ/mol.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Supplementary material for manuscript: Microcomputed X-ray Tomographic Imaging and Image Processing for Microstructural Characterization of Explosives

This data contains the supplemental information that will be accessible to the public from the paper “Microcomputed X-ray Tomographic Imaging and Image Processing for Microstructural Characterization of Explosives”. The data set contains the reconstructed slices for the 3D images of three different high explosives including: HMX-HTPB, PBX 9501 and PBX 9502. The data sets are folders of reconstructed tiffs showing the microstructure (crystals, binder, voids) and a segmented data set of each. Finally, a .gif movie is also present that plays the slices sequentially. The folders contain the voxel size information. See the manuscript for more details.

36 MATERIALS SCIENCE↗
NASA NTRS1983

Microstructural characterization of SiC (SCS) filaments

Microstructural features of SiC('SCS') fibers demonstrating growth properties have been investigated using scanning and transmission electron microscopy. An etchant of fused KOH:KNO3 was developed which adequately brought out previously undetermined features.

Wawner, F. W.↗
DOE OSTI2024

High-Throughput Microstructural Characterization and Process Correlation Using Automated Electron Backscatter Diffraction

The need to optimize the processing conditions of additively manufactured (AM) metals and alloys has driven advances in throughput capabilities for material property measurements such as tensile strength or hardness. High-throughput (HT) characterization of AM metal microstructure has fallen significantly behind the pace of property measurements due to intrinsic bottlenecks associated with the artisan and labor-intensive preparation methods required to produce highly polished surfaces. This inequality in data throughput has led to a reliance on heuristics to connect process to structure or structure to properties for AM structural materials. In this study, we show a transformative approach to achieve laser powder bed fusion (LPBF) printing, HT preparation using dry electropolishing and HT electron backscatter diffraction (EBSD). This approach was used to construct a library of > 600 experimental EBSD sample sets spanning a diverse range of LPBF process conditions for AM Kovar. This vast library is far more expansive in parameter space than most state-of-the-art studies, yet it required only approximately 10 labor hours to acquire. Build geometries, surface preparation methods, and microscopy details, as well as the entire library of >600 EBSD data sets over the two sample design versions, have been shared with intent for the materials community to leverage the data and further advance the approach. Using this library, we investigated process–structure relationships and uncovered an unexpected, strong dependence of microstructure on location within the build, when varied, using otherwise identical laser parameters.

Characterization and Analytical Technique↗