[3J08-10] Natural Barrier

[3J10] New backbone definition for radionuclide transport simulation using discrete fracture network and prediction of breakthrough curves by machine learning

*Shunya Hattori¹, Takumi Saito¹ (1. UTokyo)

Keywords:Discrete fracture network, Backbone, Breakthrough curve, Machine learning

Fractures in crystalline rock provide a migration path for radionuclides leached from the radioactive waste after geological disposal site closure. Therefore, in the simulation of radionuclide migration for safety assessment of geological disposal, the geological structure is modeled by Discrete Fracture Network (DFN) and its behavior is tracked by flowing particles. And due to the high computational load, we perform simulations by predicting and extracting the backbones, which are fracture groups with many passing particles. However, existing backbone definitions have the problem that faster particles are overestimated, and slower particles are underestimated. In this study, we proposed a new backbone definition and applied machine learning to predict the breakthrough curve using the simulation results from this backbone.