Application of Genetic Algorithm to Optimal Design of Shielding Materials for Neutron-γ Mixed Radiation Fields

doi:10.19596/j.cnki.1001-246x.8714

Abstract

Abstract:

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.

Key words: Monte Carlo, genetic algorithm, neural network, shielding design, neutron-γ mixed radiation field

CLC Number:

TL71
TL99

Wenmin HAN, Yaodong DAI, Chuqing YAO, Jiaxiang TIAN, Danfeng JIANG, Yifan ZHOU. Application of Genetic Algorithm to Optimal Design of Shielding Materials for Neutron-γ Mixed Radiation Fields[J]. Chinese Journal of Computational Physics, 2024, 41(3): 357-366.

Figures/Tables 17

References 22

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Parameter type	Set
Number of layers	4(Two hidden layers)
Training function	Trainlm
Transfer function	Tansig、logsig、purelin
Training sets∶Verification set∶Testing sets	7∶1.5∶1.5
Learning rate	0.1

Parameter type	Set
Number of layers	4(Two hidden layers)
Training function	Trainlm
Transfer function	Tansig、logsig、purelin
Training sets∶Verification set∶Testing sets	7∶1.5∶1.5
Learning rate	0.1

辐射环境	源数据
gn0.05	热中子源+0.05 MeV γ射线
gn0.1	热中子源+0.1 MeV γ射线
gn0.5	热中子源+0.5 MeV γ射线
gn1	热中子源+1 MeV γ射线
gn1.5	热中子源+1.5 MeV γ射线
gn2	热中子源+2 MeV γ射线
gn2.5	热中子源+2.5 MeV γ射线
gn3	热中子源+3 MeV γ射线
gn	热中子源+0.05、0.1、0.5、1、1.5 MeV γ射线

辐射环境	源数据
gn0.05	热中子源+0.05 MeV γ射线
gn0.1	热中子源+0.1 MeV γ射线
gn0.5	热中子源+0.5 MeV γ射线
gn1	热中子源+1 MeV γ射线
gn1.5	热中子源+1.5 MeV γ射线
gn2	热中子源+2 MeV γ射线
gn2.5	热中子源+2.5 MeV γ射线
gn3	热中子源+3 MeV γ射线
gn	热中子源+0.05、0.1、0.5、1、1.5 MeV γ射线

Parameter type	Set
Population size	100
Number of iterations	100
Generation gap	0.9
Crossover rate	0.8
Mutation rate	0.01