A fine pore-preserved deep neural network for porosity analytics of a high burnup U-10Zr metallic fuel

Wang, Haotian; Xu, Fei; Cai, Lu; Salvato, Daniele; Di Lemma, Fidelma Giulia; Capriotti, Luca; Yao, Tiankai; Xian, Min

doi:10.1038/s41598-023-48800-3

Title: A fine pore-preserved deep neural network for porosity analytics of a high burnup U-10Zr metallic fuel

Journal Article · Thu Dec 14 00:00:00 EST 2023 · Scientific Reports

DOI:https://doi.org/10.1038/s41598-023-48800-3· OSTI ID:2229931

Wang, Haotian; Xu, Fei; Cai, Lu; Salvato, Daniele; Di Lemma, Fidelma Giulia; Capriotti, Luca; Yao, Tiankai; Xian, Min

Abstract U-10 wt.% Zr (U-10Zr) metallic fuel is the leading candidate for next-generation sodium-cooled fast reactors. Porosity is one of the most important factors that impacts the performance of U-10Zr metallic fuel. The pores generated by the fission gas accumulation can lead to changes in thermal conductivity, fuel swelling, Fuel-Cladding Chemical Interaction (FCCI) and Fuel-Cladding Mechanical Interaction (FCMI). Therefore, it is crucial to accurately segment and analyze porosity to understand the U-10Zr fuel system to design future fast reactors. To address the above issues, we introduce a workflow to process and analyze multi-source Scanning Electron Microscope (SEM) image data. Moreover, an encoder-decoder-based, deep fully convolutional network is proposed to segment pores accurately by integrating the residual unit and the densely-connected units. Two SEM 250 × field of view image datasets with different formats are utilized to evaluate the new proposed model’s performance. Sufficient comparison results demonstrate that our method quantitatively outperforms two popular deep fully convolutional networks. Furthermore, we conducted experiments on the third SEM 2500 × field of view image dataset, and the transfer learning results show the potential capability to transfer the knowledge from low-magnification images to high-magnification images. Finally, we use a pre-trained network to predict the pores of SEM images in the whole cross-sectional image and obtain quantitative porosity analysis. Our findings will guide the SEM microscopy data collection efficiently, provide a mechanistic understanding of the U-10Zr fuel system and bridge the gap between advanced characterization to fuel system design.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: 22A1059-094FP

OSTI ID:: 2229931

Journal Information:: Scientific Reports, Journal Name: Scientific Reports Vol. 13 Journal Issue: 1; ISSN 2045-2322

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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