Random model for radiation shielding calculation of particle reinforced metal matrix composites and its application. (December 2020)

• Record Type: Journal Article
• Authors: Sun, Weiqiang; Hu, Huasi; Yu, Bo; Sheng, Liang; Hu, Guang; Cai, Yao; Yang, Quanzhan; Zhang, Mei; Yan, Yihong
• Is Part Of: Applied radiation and isotopes. Volume 166(2020:Dec.)
• Publication Date: 2020-12
• Subjects: Radiation shielding -- MCNP Code -- Random model -- PRMMCs -- Optimal design; Radiology -- Periodicals; Radiation -- Industrial applications -- Periodicals; Nuclear chemistry -- Periodicals; Internet resource; Periodical; 660.298
• DOI: 10.1016/j.apradiso.2020.109299
• Languages: English
• ISSNs: 0969-8043

Abstract

Abstract: The work aimed to calculate the radiation biological shielding performance of particle reinforced metal matrix composite (PRMMCs) using more reasonable model instead of conventional Uniform Filling Model, also attempted to provide a basis for the radiation shielding optimal design of such materials. Firstly, RSA (Random Sequential Adsorption) Model and GRM (Grid Random Model) were established based on MATLAB and Monte Carlo Particle transport program MCNP, and then advantages and disadvantages of them were compared. Later, the influences of metal matrix type, particle (B4 C) content, particle shape and particle shape parameters on the biological shielding performance of materials were calculated under different energy neutrons and different thickness shield using random models. Finally, the optimal aspect ratio of regular hexahedral B4 C was calculated by Genetic Algorithm combined with MATLAB and MCNP. It indicated that GRM could be applied to radiation shielding calculation of PRMMCs. Highlights: Two kinds of random model were built for radiation shielding calculation of particle reinforced metal matrix composites. Metal matrix type, particle content, particle shape and particle shape parameters were all considered in calculation. The optimal aspect ratio of regular hexahedral B4 C was calculated by Genetic Algorithm combined with MATLAB and MCNP.