Conducting a TSPA (Total System Performance Assessment) of the entire spent nuclear fuel disposal system, which includes thousands of disposal holes and their geological surroundings over many thousands of years, is a challenging task. Typically, the TSPA relies on significant efforts involving numerous parts and finite elements, making it computationally demanding. To streamline this process and enhance efficiency, our study introduces a surrogate model built upon the widely recognized U-network machine learning framework. This surrogate model serves as a bridge, correcting the results from a detailed numerical model with a large number of small-sized elements into a simplified one with fewer and large-sized elements. This approach will significantly cut down on computation time while preserving accuracy comparable to those achieved through the detailed numerical model.

• Gil-Eon Jeong(Korea Atomic Energy Research Institute (KAERI)) Corresponding author
• DongHyuk Lee(Korea Atomic Energy Research Institute (KAERI))
• Hong Jang(Korea Atomic Energy Research Institute (KAERI))
• Jung-Woo Kim(Korea Atomic Energy Research Institute (KAERI))