Predicting radionuclide behavior in deep geological repositories using graph convolutional long short-term memory

Abstract

Deep geological repositories (DGRs) are designed for the permanent disposal of spent nuclear fuel, necessitating precise radionuclide transport predictions. Owing to the impracticality of large-scale physical experiments, computational simulations are a key alternative. Although the Parallel Flow and Reactive Transport Model (PFLOTRAN) is widely used for radionuclide transport simulations, its high computational demands limit its practical application. This study employs Graph Convolutional Long Short-Term Memory (GCLSTM) as a surrogate model for PFLOTRAN to simulate radionuclide transport and significantly reduce computational costs while maintaining predictive accuracy. GCLSTM was trained using time-series data from PFLOTRAN simulations over a 5,000-year period. The model achieved a coefficient of determination above 0.99 and a Nash-Sutcliffe efficiency exceeding 0.97 at all observation nodes. Combined uncertainty quantification and sensitivity analyses demonstrate that over 95 % of GCLSTM predictions fall within PFLOTRAN-derived confidence intervals and that permeability and inter-node distance are the primary drivers of predictive variance. Additionally, scenario-based simulations validated the adaptability of GCLSTM to varying prediction lengths and release conditions. By reducing the computational time by approximately 576 times compared to that of PFLOTRAN while maintaining predictive accuracy, GCLSTM demonstrated its potential as an efficient and reliable alternative. This approach enhances modeling efficiency by utilizing GCLSTM as a surrogate for PFLOTRAN, offering a practical solution for long-term radionuclide transport simulations.