Application of Genetic Algorithm to Optimal Design of Shielding Materials for Neutron-γ Mixed Radiation Fields
Based on the neutron-γ mixed radiation field,the metal oxide filler components in the material are optimized,and the comprehensive shielding performance of WO3/Bi2O3/Gd2O3/B4C mixed filler against low energy neutrons and different energy γ rays is obtained by Monte Carlo simulation.The optimal ratio of filler components is found by using genetic algorithm and neural network.Through the calculation and optimization of the total dose equivalent,it is found that the optimal ratio is different under different radiation environments.And the comprehensive shielding performance can be optimized by using Bi2O3 and B4C(9∶1)mixed fillers when the neutron(thermal neutron Maxwell distribution spectrum)flux is equal to γ ray(0.5-3 MeV)flux and the total mass of the shielding filler is constant.The results of Monte Carlo program show that the error is within an acceptable range,which indicates that the optimal design of the shielding filler is feasible.It can save a lot of calculation time and provide a theoretical basis for the design and preparation of shielding materials.
Monte Carlogenetic algorithmneural networkshielding designneutron-γ mixed radiation field
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