The first irradiation testing and PIE or TRISO-coated particle fuel in Korea

Kim, Bong Goo; Yeo, Sunghwan; Jeong, Kyung-Chai; Kim, Yeon-Ku; Lee, Young Woo; Cho, Moon Sung

doi:10.1016/j.nucengdes.2018.01.037

Cited by 3 publications

(2 citation statements)

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“…Currently, ceramography is the most widely used method to investigate such microstructures . Similarly, scanning electron microscopy (SEM) analysis is also a destructive technique used widely to perform cross‐sectional analysis . In terms of nondestructive inspection, X‐ray phase‐contrast imaging (PCI) is preferred over conventional X‐ray imaging because it can image adjacent coating layers with sharp interfaces without sacrificing time in the examination of TRISO fuel particles .…”

Section: Introductionmentioning

confidence: 99%

“…5,6 Similarly, scanning electron microscopy (SEM) analysis is also a destructive technique used widely to perform cross-sectional analysis. [7][8][9] In terms of nondestructive inspection, X-ray phase-contrast imaging (PCI) is preferred over conventional X-ray imaging because it can image adjacent coating layers with sharp interfaces without sacrificing time in the examination of TRISO fuel particles. 10,11 Woong Ki Kim et al first applied PCI to visualize the structure of each coating layer, then the Sobel operator and a 3 × 3 average filter were used to extract interfaces between coating layers.…”

Section: Introductionmentioning

confidence: 99%

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A novel method to inspect coating thickness of tristructural isotropic fuel particles

et al. 2019

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Summary X‐ray phase‐contrast imaging (PCI) is a nondestructive method to measure the coating thickness of tristructural isotropic (TRISO) fuel particles, with the advantages of generating sharp interfaces between two coating layers. However, the analysis of PCI images is hindered by noise and subject to operator‐dependence. The aim of this study was to develop a measurement method that can exclude frustrating noise corruption and achieve computerized automation. The total variation (TV) algorithm was applied to reduce the unnecessary information, and the denoising results were optimized with the introduction of contrast‐to‐noise ratio (CNR). Contours of denoised images were automatically extracted using an adaptive Canny operator, employing the P‐tile method. Thickness data, derived using the proposed method, were compared with those of ceramography, and the results were confirmed by performing the one‐way analysis of variance (ANOVA) analysis. This measuring method allowed the denoising process to restore images with appropriate edge features and detect edges with more sharpness and continuity in minimal time. When evaluated against ceramography, there was a minor discrepancy (less than 10%) in all coating layers. The one‐way ANOVA analysis exhibited the rejection of the significant differences in the thicknesses measured by the two aforementioned methods. The proposed method seems promising for practical application of such nondestructive measurements.

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Section: Introductionmentioning

confidence: 99%