Application of deep neural network for generating resonance self-shielded cross-section

• DOI: 10.1016/j.anucene.2020.107785
• Publication Date: 2020-09-06T06:47:19Z
• Authors: Shuai Qin; Qian Zhang; Jinchao Zhang; Liang Liang; Qiang Zhao; Hongchun Wu; Liangzhi Cao
• Keywords: 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology

Abstract In this paper, the deep learning based on the artificial neural network (ANN), which is referred to as the deep neural network (DNN), is adopted to build a new model for the generation of the resonance self-shielded cross-sections (XSs). In this model, using the dataset generated from the pin-based ultra-fine-group (UFG) calculations under a multi-dimensional parameter table, the multi-layer DNN is trained to learn the underlying relationship between resonance self-shielded XSs and correlated parameters. Then the trained DNN is used for further practical calculations, which takes a negligible computing time. The computing accuracy of this model is tested through the generated datasets and practical PWR problems, and numerical results show that the new model is a promising approach for the generation of the resonance self-shielded XSs.