In this paper, a method based on a backpropagation neural network (BPNN) is proposed to calculate the exposure buildup factor (BD) of a point isotropic source in an infinite homogeneous medium under arbitrary energy and mean free path (mfp). The results obtained for aluminum, iron, lead, and concrete based on BPNN are compared to ANSI/ANS-6.4.3 standard data, the results calculated by MCNP 5 Monte Carlo code, and a geometric progression (G-P) fitting formula, and show that the BD calculated by the BPNN model is more consistent with the ANS standard data. This method improves the calculation and fitting effect of BD compared to other methods. This paper proposes a systematic process combining a Monte Carlo method and BPNN to calculate and predict the BD of new materials under different energy and mfp, thus replacing the G-P fitting formula and improving calculation accuracy.
Calculation of gamma-ray exposure buildup factor based on backpropagation neural network
Cammi A.;
2021-01-01
Abstract
In this paper, a method based on a backpropagation neural network (BPNN) is proposed to calculate the exposure buildup factor (BD) of a point isotropic source in an infinite homogeneous medium under arbitrary energy and mean free path (mfp). The results obtained for aluminum, iron, lead, and concrete based on BPNN are compared to ANSI/ANS-6.4.3 standard data, the results calculated by MCNP 5 Monte Carlo code, and a geometric progression (G-P) fitting formula, and show that the BD calculated by the BPNN model is more consistent with the ANS standard data. This method improves the calculation and fitting effect of BD compared to other methods. This paper proposes a systematic process combining a Monte Carlo method and BPNN to calculate and predict the BD of new materials under different energy and mfp, thus replacing the G-P fitting formula and improving calculation accuracy.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
