Machine Learning-Based Inversion of Axial-Segment Characterization for Spent Fuel Materials

The burnup, initial enrichment, and cooling time of spent nuclear fuel collectively determine the activities of key gamma-emitting nuclides (e.g., 134Cs, 137Cs, 154Eu). In safeguards verification, a non-destructive assay (NDA) using radiation detectors can directly acquire the gamma-ray emission signatures associated with these characteristic nuclides. Previous studies have reported empirical relationships between the activities of nuclides such as 134Cs, 137Cs, and 154Eu and the assembly burnup. However, the non-uniform axial power distribution in fuel assemblies leads to variations in axial-segment burnup. Accordingly, this study utilizes a nuclide sample database of a typical pressurized water reactor (PWR) assembly generated by OpenMC 0.15.3 depletion calculations. The calculated results are analyzed, and a sensitivity analysis of the hydrogen-to-uranium atomic ratio (H/U) on the characteristic nuclides is presented, confirming the necessity of incorporating the H/U ratio as an input parameter to improve the cross-condition generalization of the surrogate models. Subsequently, MLP and CNN based on PyTorch 2.9.1 (CUDA 13.0 build: 2.9.1+cu130), and XGBoost 3.0.2 models are implemented to invert axial-segment burnup, initial enrichment, and the number densities of selected actinides under various discrete operating conditions based on characteristic nuclide activities. A comparative analysis of the prediction results from different feature inversion methods is provided. The results indicate that the MLP model performs best with Method A, which incorporates absolute 137Cs activity and the 154Eu/137Cs ratio, achieving a relative prediction deviation of only 5.2% for initial enrichment. Under Method C, the XGBoost model attains a relative prediction deviation of only 0.9% for axial-segment burnup (BU_zone).