Research on Data-driven Intelligent Optimization Design of Nuclear Reactor Shielding

In order to expedite the design process of lightweight reactor shielding, a multi-objective intelligent optimization algorithm coupled with the data-driven surrogate model is employed to optimize the operational shielding of a land-based micro-mobile reactor based on multiple constraints and engineering preferences. We initially construct the dataset by sampling advanced shielding material and geometry’s parameters in the variable-scale optimization space and train the surrogate model (SN-MscaleDNN), which consists of the multi-frequency scale neural network called MscaleDNN and the GPU-parallel 1-D neutron-photon coupling transport SN solver, to achieve stable, accurate, and efficient dose rate prediction. This model is then integrated with the NSGA-II genetic algorithm, incorporating penalty functions and engineering preference models, to achieve the final shielding optimization that satisfies multiple constraints such as safety, manufacturing, and mechanical limitations. The results confirm the surrogate model's ability to accurately predict dose rates of one shielding scheme at a millisecond level with its generalization error under 10%. Furthermore, the coupled optimization algorithm enables the efficient search for more shielding schemes that meet engineering constraints and preferences, thereby offering novel insights into the lightweight shielding optimization of micro-mobile reactors in a variable-scale space.