Enhancing predictive modeling in reactor building dose distribution: A neural network-aided approach

Abstract

Ensuring the safety of nuclear reactor decommissioning workers requires accurate, real-time predictions of radiation dose rates within reactor buildings. However, due to the complexity of these structures, such predictions are computationally intensive and time consuming. In this paper, we propose constructing a surrogate model using deep learning to predict radiation dose rates based on simulation results in a space containing a square pillar and a radiation source. The accuracy of the surrogate model's predictions was verified and visualized. Additionally, by applying the principle of superposition, we demonstrated that the distribution of radiation dose rates in spaces with a pillar and multiple radiation sources can be obtained by summing the surrogate model results for each radiation source. We also examined the application of the surrogate model to predicting radiation dose rates in spaces containing multiple square pillars and multiple radiation sources. This approach shows the potential for surrogate models to accurately and efficiently predict radiation dose rates in reactor buildings with complex structures and multiple radiation sources in real time.

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