Investigation of neutron-induced reaction cross section calculations for the fusion reactor structural materials using artificial neural networks

Abstract

Using artificial neural networks for an estimation of the nuclear reaction cross section data is discussed. Approximately rate of the fitting criteria is determined by the calculated experimental data obtained from using Variable Learning Rate Backpropagation (traingdx) algorithm in artificial neural networks. This method has been applied to obtain the cross section for 14–15 MeV neutron-induced (n,α) and (n,p) reactions in the fusion reactor structural materials. In comparison to the reaction cross section calculation by experimental cross sections reported in EXFOR, TALYS 1.9 and EMPIRE 3.2, the proposed method has better prediction ability when the target output has a large variation between the experimental and the calculated data. This study is substantial for the new method validation development of the nuclear model approaches with the increased prediction power of the neutron-induced reactions for fusion reactor systems.