A Monte Carlo Simulation Dataset of Radiation Field Parameters for a Simple Nuclear Facility Scenario

Abstract

Abstract Radioactive safety in nuclear facilities is of utmost importance. Prior to workers entering these areas, a 3D radiation field is needed for accurately estimating their exposure. Due to the complex relationship between radiation measurements and radiation fields, implementing neural networks is a promising approach for reconstruction. However, research on direct 3D radiation field reconstruction using neural networks is limited, and there is no standardized open-source dataset for training and evaluation. To address these issues, we created a simplified model of a nuclear facility and utilized the Monte Carlo program MCShield to simulate 3D radiation parameters. MCShield, which is mainly used for shielding calculations, has been verified for accuracy through benchmark tests. In addition, this paper proves the correctness of the MCShield program and the effectiveness of the AIS variance reduction method through calculations on the WinFrith Iron benchmark experiment and the NUREG/CR-6115 benchmark. The results show that the MCShield program as well as the AIS method can be used for dataset calculations.