Multi-Scale Characterization of Porosity and Cracks in Silicon Carbide Cladding after Transient Reactor Test Facility Irradiation

Fei Xu (), Tiankai Yao, Peng Xu (), Jason L. Schulthess, Mario D. Matos, Sean Gonderman, Jack Gazza, Joshua J. Kane and Nikolaus L. Cordes
Additional contact information
Fei Xu: Idaho National Laboratory, Idaho Falls, ID 83415, USA
Tiankai Yao: Idaho National Laboratory, Idaho Falls, ID 83415, USA
Peng Xu: Idaho National Laboratory, Idaho Falls, ID 83415, USA
Jason L. Schulthess: Idaho National Laboratory, Idaho Falls, ID 83415, USA
Mario D. Matos: Idaho National Laboratory, Idaho Falls, ID 83415, USA
Sean Gonderman: General Atomics, San Diego, CA 92121, USA
Jack Gazza: General Atomics, San Diego, CA 92121, USA
Joshua J. Kane: Ultra Safe Nuclear Corporation, Seattle, WA 98199, USA
Nikolaus L. Cordes: Los Alamos National Laboratory, Los Alamos, NM 87545, USA

Energies, 2023, vol. 17, issue 1, 1-15

Abstract: Silicon carbide (SiC) ceramic matrix composite (CMC) cladding is currently being pursued as one of the leading candidates for accident-tolerant fuel (ATF) cladding for light water reactor applications. The morphology of fabrication defects, including the size and shape of voids, is one of the key challenges that impacts cladding performance and guarantees reactor safety. Therefore, quantification of defects’ size, location, distribution, and leak paths is critical to determining SiC CMC in-core performance. This research aims to provide quantitative insight into the defect’s distribution under multi-scale characterization at different length scales before and after different Transient Reactor Test Facility (TREAT) irradiation tests. A non-destructive multi-scale evaluation of irradiated SiC will help to assess critical microstructural defects from production and/or experimental testing to better understand and predict overall cladding performance. X-ray computed tomography (XCT), a non-destructive, data-rich characterization technique, is combined with lower length scale electronic microscopic characterization, which provides microscale morphology and structural characterization. This paper discusses a fully automatic workflow to detect and analyze SiC-SiC defects using image processing techniques on 3D X-ray images. Following the XCT data analysis, advanced characterizations from focused ion beam (FIB) and transmission electron microscopy (TEM) were conducted to verify the findings from the XCT data, especially quantitative results from local nano-scale TEM 3D tomography data, which were utilized to complement the 3D XCT results. In this work, three SiC samples (two irradiated and one unirradiated) provided by General Atomics are investigated. The irradiated samples were irradiated in a way that was expected to induce cracking, and indeed, the automated workflow developed in this work was able to successfully identify and characterize the defects formation in the irradiated samples while detecting no observed cracking in the unirradiated sample. These results demonstrate the value of automated XCT tools to better understand the damage and damage propagation in SiC-SiC structures for nuclear applications.

Keywords: silicon carbide; crack and defects detection; X-ray CT; FIB; TEM 3D tomography; electron microscopy; data visualization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/1/197/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/1/197/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2023:i:1:p:197-:d:1310140

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().