Machine learning approach for predicting nuclide composition in nuclear fuel: Bypassing traditional depletion and transport calculations

Accurate prediction of nuclide composition evolution during fuel depletion typically requires computationally intensive transport calculations to determine 1-group cross sections for all transmutation reactions in the reaction network. These cross sections are influenced by the neutron energy spectrum, which varies with both depletion and changing state parameters, such as fuel and moderator temperatures. This study investigates the feasibility of a machine learning-based model designed to predict nuclide composition directly from power history and state parameters, bypassing the need to calculate group cross sections and solve the Bateman equation. Model testing on an unseen power and state parameter history showed a mean absolute relative error of 0.471% for 235 U. Due to its rapid inference speed, this approach is well suited to applications where traditional transport calculations are prohibitively expensive. • An ML model is designed to predict nuclide composition from state parameter history • We use neural networks with external recurrence to predict the nuclide composition. • To increase model robustness, we blend two neural networks using learnable parameters