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FLiNaK-ThF_(4)的微观结构及热力学性质的深度势能分子动力学研究

Deep potential molecular dynamics on the microstructure and thermodynamic properties of FLiNaK–ThF_(4)
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摘要 钍基熔盐堆是六种第四代先进反应堆之一,深入研究熔盐堆燃料的基础物性对反应堆的安全有效运行和乏燃料后处理意义重大.首先,使用深度神经网络对FLiNaKTh熔盐燃料的力场进行了深度学习,得到了高精度的力场模型.其次,系统研究了FLiNaKTh燃料的配位结构、热力学和动力学性质.径向分布函数和平均力势表明各离子对的相互作用强弱为:Th–F>Li–F>Na–F>K–F,且均随ThF4浓度增大或升温而变弱.在燃料服役温度900 K下,FLiNaK–10 wt.%ThF_(4)体系中Th离子主要以[ThF_(x)]^(4–x)形式存在,Th浓度增大有利于Th离子间形成团簇,导致[Th_(2)F_(x)]^(8–x)和[Th_(3)F_(x)]^(12–x)等占比增大.模拟的扩散系数、热容和密度值与实验值吻合的很好,证明了力场的精确性,进一步预测了0~50wt.%ThF_(4)浓度范围和更多温度下熔盐体系的热物理性质. Thorium-based molten salt reactors are one of the six fourth generation advanced reactors,and an in-depth study of the underlying physical properties of molten salt reactor fuel is of great significance for the safe and effective operation of the reactor and spent fuel reprocessing.Firstly,the force field of FLiNaKTh-based molten salt fuel was deeply learned using deep neural networks,and a high-precision force field model was obtained.Secondly,the coordination structure,thermodynamic,and kinetic properties of FLiNaKTh fuel were systematically investigated.The radial distribution function and the average force potential show that the interaction strength of each ion pair is:Th–F>Li–F>Na–F>K–F,and all of them become weaker with increasing ThF_(4) concentration or temperature.At the fuel service temperature of 900 K,the Th ions in the FLiNaK–10 wt.%ThF_(4) system exist mainly as[ThF_(x)]^(4–x).Increasing Th concentration facilitates the formation of clusters between Th ions,leading to an increased percentage of[Th_(2)F_(x)]^(8–x)and[Th_(3)F_(x)]^(12–x),etc.The simulated diffusion coefficient,heat capacity,and density values agree well with the experimental values,proving the accuracy of the force field.We further predicted the thermophysical properties of the molten salt system in the ThF_(4) concentration range of 0~50 wt.%and at higher temperatures.
作者 尹晚秋 薄涛 赵玉宝 张蕾 柴之芳 石伟群 Wanqiu Yin;Tao Bo;Yubao Zhao;Lei Zhang;Zhifang Chai;Weiqun Shi(School of Chemistry and Chemical Engineering,University of South China,Hengyang 421001,China;Engineering Laboratory of Advanced Energy Materials,Ningbo Institute of Materials Technology and Engineering,Chinese Academy of Sciences,Ningbo315201,China;Qianwan Institute of CNITECH,Ningbo 315336,China;Laboratory of Nuclear Energy Chemistry,Institute of High Energy Physics,Chinese Academy of Sciences,Beijing 100049,China)
出处 《中国科学:化学》 CAS CSCD 北大核心 2023年第6期1008-1019,共12页 SCIENTIA SINICA Chimica
基金 国家自然科学基金资助项目(编号:12105196,U2267222,U2167218)。
关键词 钍基熔盐堆 氟化钍 机器学习 分子动力学 微观结构 热物理性质 thorium-based molten salt reactor thorium fluoride machine learning molecular dynamics microstructure thermophysical properties
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