Metallic U-Zr nuclear fuel alloy has regained interest as a promising candidate for next generation sodium-cooled fast reactors. Recent studies and technology assessments have identified several areas where improvements, enhanced knowledge, and reliable data are needed to bolster the U-Zr fuel design basis for qualification and commercial use. One of the most constraining phenomena affecting this fuel system’s performance is fuel-cladding chemical interaction (FCCI). This work aimed to harvest FCCI data by examining selected HT9/U-10Zr fuel samples of prototypic full-length through a novel approach for FCCI characterization and quantification. This approach combined scanning electron microscopy (SEM) microstructure characterization with localized mechanical properties examination study to deepen understanding of FCCI phenomenon in HT9/U-10Zr fuel system. Electron microscopy provided a high confidence in detecting and distinguishing the different wastage layers, while small-scale mechanical testing (SSMT) probed the local mechanical properties of these layers. SEM examination of a relatively cold fuel pin, MFF-2 1921678 revealed very minimum to moderate cladding wastage, while much thicker cladding wastage of two distinctive sublayers was observed in samples from hot MFF-3 and MFF-5 fuel pins. Additionally, a new machine learning method was developed, validated, and used to quantify cladding-side FCCI (cladding wastage) thickness. The machine learning method reliably predicted the wastage thickness across various fuel pins and sample cross-sections. The available cladding wastage data from HT9/U-10Zr fuel system demonstrated a strong temperature dependency. However, the dataset is still small, and continuous research activities are necessary to further understand the FCCI phenomenon and develop a reliable FCCI model for improved fuel performance simulation under various conditions.
