Segmentation and Classification of Fission as Pores in Reactor Iirradiated Annular U[sbnd]10Zr Metallic Fuel Using Machine Learning Models

Abstract

Metallic fuels, particularly U[sbnd]10Zr, are promising candidates for next-generation sodium-cooled fast reactors. Irradiation of nuclear fuels in reactors can lead to the formation of solid and gas fission product which subsequently forms microstructural pores, deteriorating fuel performance. Due to the massive amount of pores and complex phases formed, a quantitative description of fission gas pores is not yet available, preventing the development of microstructure-informed fuel performance modeling for fuel qualification. This paper applied a pre-trained deep learning model to ∼10,260 high magnification scanning electron microscopy images. This method increased the accuracy of fission gas pore segmentation and allows statistical features to be extracted which cannot be achieved manually. A pre-trained decision tree model worked on the segemenation resutls and further classified the pores into different categories to produce a correlation between the pores, movement of lanthanides, and temperature gradient during irradiation. This paper emphasizes the potentials of machine learning models to accelerate fuel research, development, and qualification for advanced reactors.