Prediction of radiation shielding design schemes based on adaptive neural networks

Abstract

As space exploration and space reactor technology continue to advance, radiation shielding design faces numerous challenges, such as space limitations, weight constraints, and the complexity of shielding materials. Traditional design methods typically rely on empirical models validated through Monte Carlo simulations, but these approaches often fail to achieve optimal results. To enhance the radiation protection efficiency of space reactors, this paper proposes a deep neural network model based on the self-attention mechanism to assist in predicting radiation shielding design schemes and to verify its accuracy and practicality. We used SuperMC software to record the relevant safety parameters for the Kilopower reactor under 10,000 different shielding design schemes and calculated the total mass and total radiation dose for these designs, creating a comprehensive dataset. The total mass and radiation dose were used as inputs to the neural network, which then generated the corresponding radiation shielding design schemes. Experimental results show that the model demonstrates high accuracy and strong interference resistance, with the error in total radiation dose and material mass consistently controlled around 3%. Additionally, by combining simulation methods with the self-attention mechanism, the model effectively generates radiation shielding designs suitable for space reactors, providing reliable protection solutions for future space missions. This approach also opens new possibilities for radiation shielding design in other fields.