基于天鹰算法与BP神经网络混合模型的X射线柔性材料屏蔽性能优化OA

The performance optimization of radiation shielding materials remains a central focus in the field of radiation protection.Traditional approaches to shielding material design have relied heavily on extensive experimental data and empirical knowledge,which is not only time-consuming and costly,but also cannot guarantee identification of globally optimal solutions.This study proposes a strategy combining the Aquila·114·Optimizer(AO)with a BP neural network to optimize the composition of X-ray shielding materials and achieve efficient shielding across different X-ray energy segments.Initially,Monte Carlo simulations are employed to establish an X-ray tube model.Functional elements featuring complementary K-absorption edge characteristics are screened via the XCOM program,and Monte Carlo calculations determine the shielding rate for various elemental proportions.Subsequently,a BP neural network is employed to model the non-linear mapping between input parameters(elemental composition)and output parameters(shielding performance).SHAP(SHapley Additive Explanations)interpretability is applied to quantify the contribution of each element to the shielding rate.The AO algorithm is subsequently employed to determine the optimal elemental proportion scheme.Finally,Monte Carlo simulations are utilized for performance testing and comparative analysis of the optimized composition.Results indicate that for the composition W∶Bi∶Gd∶Sm∶SEBS=0.018 8∶0.261 0∶0.058 1∶0.162 1∶0.500 0,a shielding rate of 75.51%is achieved at 100 kV tube voltage,with a material density of 1.600 7 g/cm3.Additionally,an in-depth investigation of optimal functional element proportions for different energy segments was performed.This method demonstrates significant innovation and effectiveness,thus enriching the computational methods for research,development,and application optimization of composite shielding materials.