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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Image processing workflow yielding high contrast synchrotron nanoscale computed tomography data from Ni-YSZ electrodes

The operating lifetime of Ni-YSZ fuel electrodes used in solid oxide electrolysis cells and fuel cells (SOECs and SOFCs) is limited by Ni redistribution, one of the primary degradation mechanisms that must be overcome to extend the longevity and maximize the performance of SOECs and SOFCs. To achieve this, 3D microstructural data is needed to relate both initial performance and performance loss over time to microstructural properties and their evolution throughout operation under various conditions. However, 3D microstructure data remains relatively scarce within the literature due to multiple challenges in acquiring and analyzing such data reliably. This work presents a workflow for acquiring and processing synchrotron X-ray nanoscale computed tomography (nano-CT) data from Ni-YSZ electrodes. Parameters for each step in the nano-CT workflow are described up to the final result (a 3D reconstruction), with particular emphasis on image alignment using freely available software. Following the results of a parametric sweep of the image alignment step, high contrast, low signal-to-noise 3D nano-CT data is obtained with relatively short compute times. While the exact methods best suited to samples with different microstructural qualities, or similar Ni-YSZ nano-CT data obtained from other sources may deviate from the solution found herein, this work also generalizes the decision points and evaluation of each step to provide a starting point to adapt this workflow to other datasets.

08 HYDROGEN↗
DOE OSTI2025

3D pore structure reconstruction of fluff region in top of EBR-II irradiated fuel by X-ray micro-computed tomography

Metallic fuels comprised of U-Zr binary and U-Pu-Zr ternary alloys are a promising fuel candidate for sodium-cooled fast reactors and possess many advantages over ceramic fuels such as higher uranium density and improved thermal conductivity. Fluff is a highly porous structure forming at the top of metallic fuel slugs during irradiation and was extensively observed in past sodium-cooled fast reactor (SFR) experiments such as Idaho National Laboratory’s Experimental Breeder Reactor II (EBR-II). Previous investigations have shown that fluff is rich in fissile atoms and hence could have neutronics implications, but its formation mechanisms are not understood. In this work, two cube-shaped lift outs were taken from the fluff and bulk fuel regions of a high burnup U-19Pu-10Zr composition EBR-II fuel slug and characterized by micro computed tomography (Micro-CT) and energy dispersive x-ray spectroscopy (EDS). This was used to characterize differences in microscale porosity and composition between fluff and bulk fuel, and to support or oppose postulated fluff formation mechanisms. Surface EDS showed minimal differences between the elemental compositions of the fluff and fuel cubes. However, three-dimensional (3D) pore analysis from the tomography scans showed significant differences between the fluff and fuel cube pore size distributions at the microscale level. The high porosity and irregular pore distribution seen in macroscale fluff was also present within the micro-scale fluff cube, having a much higher porosity than the fuel cube which was concentrated in a few large pore networks. In conclusion, these results indicate support for a creep driven mechanism of fluff formation.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Brady’s Geothermal Field Computed Tomography Core Characterization

We present a publicly accessible database centered around multi-scale Computed Tomographic (CT) imaging of samples from the BCH-03 well, supplemented by a wide range of supporting geological analyses (Brown, et al., 2022). The data include detailed petrologic descriptions of core samples, core-scale medical CT scans, high-resolution micro-CT scans, thin section photomicrographs, elemental abundance data from X-ray diffraction, velocity wave measurements, and helium porosimetry of the matrix.

computed tomography↗
DOE OSTI2025

Boosting Noise2Inverse via enhanced model selection for denoising computed tomography data

Synchrotron-based x-ray tomographic imaging enables the examination of the internal structure of materials at high spatial and temporal resolution. Experimental constraints can impose dose and time limits on the measurements, introducing a higher level of noise and artifacts in the reconstructed images. Deep learning has emerged as a powerful tool to remove noise from reconstructed images. Recently, the Noise2Inverse method was designed specifically for denoising reconstructed images without requiring paired noisy and clean images. This method creates multiple statistically independent reconstructions used to pair the data in which training involves transforming one reconstruction into the other, and vice versa. Originally designed to be used after a fixed number of epochs, we see in practice that this approach may not produce the optimal model and may unnecessarily waste computational resources. Therefore, we propose an alternative method of identifying the best model during training that aligns with the Noise2Inverse method. During validation, we compare the model output of the multiple reconstructions among each other. We hypothesize that the best model is the one that produces images with the highest similarity, implying a convergence in the predicted material properties and absorption values. To compare model outputs, we consider the absolute error, square error, structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and cosine similarity. We evaluate our method on two simulated tomography datasets and two, real-world, low-contrast, high-energy x-ray tomography datasets. We show our approach is more effective at determining the best model, up to an increase of 12.50% and 12.53% in SSIM and PSNR, respectively, while only requiring a fifth of the training time compared to the original approach.

CT↗
DOE OSTI2025

Livermore Computed Tomography Input/Output library

Read, write, and connect visualizers to data for LLNL radiography and CT tools (e.g. LTT: https://softwarelicensing.llnl.gov/product/livermore-tomography-tools-ltt).

Vardar-Irrgang, MichaelE [Lawrence Livermore Natio↗
DOE OSTI2025

Using multimodal X-ray computed tomography to advance 3D petrography: A non-destructive investigation of olivine inside a carbonaceous chondrite

Rocks form in three dimensions through time and studying them provides information from inside dynamic systems we cannot otherwise observe. Yet how we typically access the interior of the rocks themselves to gain that information may limit our understanding and influence how we reconstruct the processes that formed them. Here, we demonstrate combined non-destructive 3D X-ray imaging techniques that produce quantitative densitometric and crystallographic maps of entire individual grains inside a rock. Olivine grains throughout a sample of the carbonaceous chondrite Northwest Africa (NWA) 11346 were each characterized by size, shape, composition, zoning intensity, and crystallographic orientation. The addition of 3D crystallographic mapping to calibrated 3D densitometric analysis—used to calculate chemical composition—demonstrates a fully non-destructive petrographic method and provides unique insight. For instance, in our case, using crystallographic data to delineate individual grains and then measuring the 3D size, shape, and composition of each distinguishes variably reset relict grains from those later crystallized after a melting event. Intersection in a 2D slice could not have led to this interpretation because the integration of three-dimensional size, rounding, composition, location, and crystallographic orientation measured from each grain forms the key patterns. Multimodal laboratory X-ray imaging has strong potential to advance 3D petrography.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Leveraging Afterglow in Scintillation-based x-ray detectors for spacetime-resolved computed tomography for accelerated acquisition and high-speed event capture

Afterglow in x-ray imaging for high-speed radiography is a constraint that limits imaging systems to low-light/fast decay screens which create poor data. Current approaches focus purely on using low-light yield screens with fast decay to avoid multiple exposure pileup due to afterglow. The goal of this work is to develop a statistical estimation approach to leverage afterglow to improve image quality thus allowing for higher quality imaging components to be used. This will allow for bright screens will slow decay to be used, and then a post-processing step applies the statistical estimation to separate each frame with superior signal compared to low-light/fast decay screens.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

2.5D Super-Resolution Approaches for X-Ray Computed Tomography-Based Inspection of Additively Manufactured Parts

X-ray computed tomography (XCT) is a key tool in non-destructive evaluation of additively manufactured (AM) parts, allowing for internal inspection and defect detection. Despite its widespread use, obtaining high-resolution CT scans can be extremely time consuming. This issue can be mitigated by performing scans at lower resolutions; however, reducing the resolution compromises spatial detail, limiting the accuracy of defect detection. Super-resolution algorithms offer a promising solution for overcoming resolution limitations in XCT reconstructions of AM parts, enabling more accurate detection of defects. While 2D super-resolution methods have demonstrated state-of-the-art performance on natural images, they tend to under-perform when directly applied to XCT slices. On the other hand, 3D super-resolution methods are computationally expensive, making them infeasible for large-scale applications. To address these challenges, we propose a 2.5D super-resolution approach tailored for XCT of AM parts. Our method enhances the resolution of individual slices by leveraging multi-slice information from neighboring 2D slices without the significant computational overhead of full 3D methods. Specifically, we use neighboring low-resolution slices to super-resolve the center slice, exploiting inter-slice spatial context while maintaining computational efficiency. This approach bridges the gap between 2D and 3D methods, offering a practical solution for high-throughput defect detection in AM parts.

Sullivan, Haley↗
DOE OSTI2024

Dual X-ray computed tomography-aided classification of melt pool boundaries and flaws in crept additively manufactured parts

In metal additive manufacturing (AM), understanding the process-structure-performance relationships requires a combination of multi-scale characterization techniques that allows for the measurement of the melt pool shape and boundary and classifying various defects and flaws in the AM parts. Such approaches can be destructive, only 2D in nature, or have a small field of view and can be complex to co-register and analyze. Here, in this work, we present a non-destructive 3D inspection technique that employs dual-energy X-ray computed tomography (XCT) along with a model-based iterative reconstruction (MBIR) and a new segmentation algorithm. The proposed approach and algorithm are not only capable of classifying and quantifying flaws such as pores, cracks, and inclusions, but they also allow for the extraction of microstructural features such as melt pool boundaries (MPB) and melt pool regions (MPR), that can help understand process-structure-performance relationships for alloys under study. As an exemplar application, we employed the method for characterization of an additively manufactured aluminum alloy crept under tensile stress at 300 °C for 1064 h. Our results demonstrate high quality segmentation and classification of various flaws and MPB and MPR, for the first time, using 3D X-ray CT inspection. The delineated MPB and MPR in the crept samples reveal the preferential growth paths of cracks that formed during creep deformation. The technique was used for successfully quantifying the characteristics (number of defects, their density, volume fraction, etc.) of the manufacturing-induced pores and creep-induced cracks, which is necessary to better understand the creep failure mechanisms of the material.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Fast Hyperspectral Neutron Tomography

Hyperspectral neutron computed tomography is a tomographic imaging technique in which thousands of wavelength-specific neutron radiographs are measured for each tomographic view. In conventional hyperspectral reconstruction, data from each neutron wavelength bin are reconstructed separately, which is extremely time-consuming. These reconstructions often suffer from poor quality due to low signal-to-noise ratios. Consequently, material decomposition based on these reconstructions tends to produce inaccurate estimates of the material spectra and erroneous volumetric material separation. In this paper, we present two novel algorithms for processing hyperspectral neutron data: fast hyperspectral reconstruction and fast material decomposition. Both algorithms rely on a subspace decomposition procedure that transforms hyperspectral views into low-dimensional projection views within an intermediate subspace, where tomographic reconstruction is performed. The use of subspace decomposition dramatically reduces reconstruction time while reducing both noise and reconstruction artifacts. We apply our algorithms to both simulated and measured neutron data and demonstrate that they reduce computation and improve the quality of the results relative to conventional methods.

Chowdhury, Mohammad Samin Nur [Purdue University]↗