An Efficient Instance Segmentation Approach for Studying Fission Gas Bubbles in Irradiated Metallic Nuclear Fuel

Abstract

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. A mechanical understanding of fission gas bubble evolution behavior is a prerequisite for fuel development and qualification. 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 detail and an extensive amount of 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 multi-task deep learning network that integrates both 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 other similar material micro-structure segmentation tasks.

Deep learning-enhanced characterization of bubble dynamics in proton exchange membrane water electrolyzers

“…[14][15][16][17] Recently, Sun et al performed a study where a multi-task deep learning (DL) network was employed for instance segmentation to elucidate fission gas bubbles within nuclear fuel. 18 Anderson et al utilized DL to autonomously identify helium bubbles in irradiated micrographs and to extract their radii and cumulative volumes. The model exhibited a high accuracy, achieving a 93% success rate in the detection of bubbles on high-magnification micrographs, and maintained robust performance in analyzing lower magnification samples.…”