Post-irradiation characterization of a high burnup mixed oxide fuel rod with minor actinides

Frazer, D.; Cappia, Fabiola; Harp, Jason; Medvedev, Pavel; McClellan, Kenneth J.; Voit, Stewart L; Giglio, Jeffrey J.; Jädernäs, Daniel; Hosemann, Peter

doi:10.1016/j.jnucmat.2022.153545

Cited by 3 publications

(1 citation statement)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, only a few experiments have used electron back-scattered diffraction (EBSD) to study this phenomenon. This is mainly because only a few hot laboratories have access to such a device for irradiated fuel studies on LWR (Bengstsson et al 15 , Jädernäs et al 16 , Gerczak et al 17 , Noirot et al 18 , Cappia et al 19 ) and on FR (Teague et al 20 , Frazer et al 21 ). In the same burn-up range in which HBS forms on the rim of the LWR UO 2 fuel pellets, micrometric or submicrometric fission gas bubbles form in the central area of the pellets.…”

Section: Introductionmentioning

confidence: 99%

Restructuring in high burn-up UO2 fuels: Experimental characterization by electron backscattered diffraction

Zacharie-Aubrun

Dowek

Noirot

et al. 2022

Journal of Applied Physics

View full text Add to dashboard Cite

This paper discusses the use of electron backscattered diffraction to characterize restructuring in a set of UO2 samples, irradiated in a pressurized water reactor at a burn-up between 35 and 73 GWd/tU, including standard UO2 samples and Cr-doped UO2 samples, to provide a better understanding of restructuring occurring both on the periphery and in the center of high-burn-up pellets. The formation of a high burn-up structure on the periphery of high burn-up UO2 was confirmed in our experiment. We found restructuring associated with bubble formation of all the samples in the central area, with higher irradiation temperatures when the burn-up exceeded 61 GWd/tU, regardless of their initial microstructure. This restructuring tended to progress with the increasing burn-up and to sub-divide the initial grains into sub-grains, with orientations close to that of the parent grains. Radial changes and differences between these samples showed that the burn-up and the temperature were not the only relevant parameters involved in restructuring.

show abstract

Section: Introductionmentioning

confidence: 99%

Restructuring in high burn-up UO2 fuels: Experimental characterization by electron backscattered diffraction

Zacharie-Aubrun

Dowek

Noirot

et al. 2022

Journal of Applied Physics

View full text Add to dashboard Cite

show abstract

Assessing the PLEIADES/GERMINAL V3 fuel performance code against transmutation experiments in sodium fast reactors

Barani,

Bernachy-Barbe,

Chauvin

et al. 2024

Annals of Nuclear Energy

View full text Add to dashboard Cite

An efficient instance segmentation approach for studying fission gas bubbles in irradiated metallic nuclear fuel

Sun,

Xu,

Cai

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Gaseous fission products from nuclear fission reactions tend to form fission gas bubbles of various shapes and sizes inside nuclear fuel. The behavior of fission gas bubbles dictates nuclear fuel performances, such as fission gas release, grain growth, swelling, and fuel cladding mechanical interaction. Although mechanical understanding of the overall evolution behavior of fission gas bubbles is well known, lacking the quantitative data and high-level correlation between burnup/temperature and microstructure evolution blocks the development of predictive models and reduces the possibility of accelerating the qualification for new fuel forms. Historical characterization of fission gas bubbles in irradiated nuclear fuel relied on a simple threshold method working on low-resolution optical microscopy images. Advanced characterization of fission gas bubbles using scanning electron microscopic images reveals unprecedented details and extensive morphological data, which strains the effectiveness of conventional methods. This paper proposes a hybrid framework, based on digital image processing and deep learning models, to efficiently detect and classify fission gas bubbles from scanning electron microscopic images. The developed bubble annotation tool used a multitask deep learning network that integrates U-Net and ResNet to accomplish instance-level bubble segmentation. With limited annotated data, the model achieves a recall ratio of more than 90%, a leap forward compared to the threshold method. The model has the capability to identify fission gas bubbles with and without lanthanides to better understand the movement of lanthanide fission products and fuel cladding chemical interaction. Lastly, the deep learning model is versatile and applicable to the micro-structure segmentation of similar materials.

show abstract

Post-irradiation characterization of a high burnup mixed oxide fuel rod with minor actinides

Cited by 3 publications

References 57 publications

Restructuring in high burn-up UO2 fuels: Experimental characterization by electron backscattered diffraction

Restructuring in high burn-up UO2 fuels: Experimental characterization by electron backscattered diffraction

Assessing the PLEIADES/GERMINAL V3 fuel performance code against transmutation experiments in sodium fast reactors

An efficient instance segmentation approach for studying fission gas bubbles in irradiated metallic nuclear fuel

Contact Info

Product

Resources

About