Intelligent Optimization for Shielding of Nuclear Power Reactor Neutron-γ Mixed Radiation Based on Non-dominated Sorting Genetic Algorithm

Based on the Savannah marine nuclear power reactor, the multi-objective optimization models of neutron-γ mixed radiation were constructed which take the weight of the shielding layers and the dose rate after shielding as the optimization objectives. And the self-developed intelligent shielding optimization method that combines the non-dominated sorting genetic algorithm (NSGA-Ⅱ) and neural network was used for the multi-objective optimization models. Thereafter, pareto-optimal solutions were obtained, and a set of the optimal solutions were chosen to calculate with Monte Carlo method and neural network respectively for feasibility verification. The obtained dose rates all meet the limits within the allowable error of neural network prediction. These results show that the intelligent shielding optimization method is feasible for shielding parameters optimization of the reactor neutron-γ mixed radiation, and it can reduce the calculation time compared with the traditional pure Monte Carlo method without reducing calculation precision.