全陶瓷微封装(full ceramic microencapsulated, FCM)燃料的最终性能受制造过程中多种因素的影响,仅依赖实验手段研究这些因素的影响存在较大挑战。本研究采用有限元方法模拟FCM燃料芯块的热压烧结过程,系统分析烧结温度、加载压力、基体孔隙率以及三结构各向同性(tri-structural isotropic, TRISO)颗粒体积分数对烧结过程中各组元应力的影响。同时,借助机器学习方法对不同参数组合的模拟结果进行深入探讨,以揭示各因素与各组元应力之间的关联性,并进行实验验证。结果表明：通过有限元与机器学习方法相结合,可以定性地反映各烧结工艺参数对烧结应力的影响,其结果与实验数据一致性较好。
The final performance of full ceramic microencapsulated (FCM) fuel is affected by multiple factors during fabrication process, and it is very difficult to study the effects of these factors only through experiments. In this study, finite element method was utilized to simulate the hot press sintering process of FCM fuel pellets. The effects of multiple parameters (sintering temperature, loading pressure, matrix porosity, and volume fraction of tri-structural isotropic (TRISO) particles) on the stresses of each component during hot press sintering process were analyzed. In addition, the simulation results of different parameter combinations were analyzed further by machine learning in order to explore the correlation between the parameters and the stresses of each component, and conducted experimental verification. The results show that the combination of finite element method and machine learning is capable of qualitatively revealing the effects of parameters on the stress during sintering, the simulation is well consistent with experiment data.
[1] ZHOU W, ZHOU W Z.Enhanced thermal conductivity accident tolerant fuels for improved reactor safety: a comprehensive review[J]. Annals of Nuclear Energy, 2018,119: 66-86.
[2] 莫华均, 张伟, 吴璐, 等. 耐事故UO2基复合燃料芯块的研发进展[J]. 核动力工程, 2020, 41(2): 36-39.
MO Huajun, ZHANG Wei, WU Lu, et al.R&D progress of accident-tolerant UO2-based composite fuel pellets[J]. Nuclear Power Engineering, 2020, 41(2): 36-39.
[3] POWERS J J, LEE W J, VENNERI F, et al.Fully ceramic microencapsulated (FCM) replacement fuel for LWRs[R]. Springfield: U.S. Department of Energy, 2013.
[4] 王某浩, 步珊珊, 卢绪祥, 等. 基于COMSOL的全陶瓷微封装燃料元件建模与传热分析[J]. 动力工程学报, 2023, 43(11): 1399-1405.
WANG Mouhao, BU Shanshan, LU Xuxiang, et al.Modelling and heat transfer analysis of fully ceramic microencapsulated fuel pellet based on COMSOL software[J]. Journal of Chinese Society of Power Engineering, 2023, 43(11): 1399-1405.
[5] 王某浩, 步珊珊, 周冰, 等. 基于等效导热系数理论模型的全陶瓷微封装燃料峰值温度预测方法[J]. 核动力工程, 2024, 45(1): 41-48.
WANG Mouhao, BU Shanshan, ZHOU Bing, et al.Peak temperature prediction method based on a theoretical model of equivalent thermal conductivity for fully ceramic microencapsulated fuel[J]. Nuclear Power Engineering, 2024, 45(1): 41-48.
[6] 张旭东, 邓俊楷, 马大衍, 等. 基于有限元计算的全陶瓷微封装燃料芯块等效热导率与微结构设计[J]. 兰州理工大学学报, 2022, 48(2): 9-15.
ZHANG Xudong, DENG Junkai, MA Dayan, et al.Effective thermal conductivity and microstructure design of fully ceramic microencapsulated fuel pellet based on finite element calculations[J]. Journal of Lanzhou University of Technology, 2022, 48(2): 9-15.
[7] 张纯禹, 朱天魁, 周毅, 等. FCM燃料热-力耦合行为仿真建模和计算方法[J]. 计算机辅助工程, 2020, 29(3): 56-62.
ZHANG Chunyu, ZHU Tiankui, ZHOU Yi, et al.Computing and modeling method on thermal-mechanical coupling behavior of FCM fuel[J]. Computer Aided Engineering, 2020, 29(3): 56-62.
[8] ZHANG C, WANG Y Y, WU Y W, et al.Preliminary numerical investigation of TRISO-matrix interface debonding characteristics in fully ceramic microencapsulated fuel[J]. Annals of Nuclear Energy, 2021, 159: 108338.
[9] ZHANG C, WU Y W, HE Y N, et al.Investigation on thermo-mechanical performance of fully ceramic microencapsulated fuel[J]. Journal of Nuclear Materials, 2021, 556: 153171.
[10] WEI H Y, ZHANG J, ZHANG Y D, et al.Modeling of irradiation-induced thermo-mechanical coupling and multi-scale behavior in a fully ceramic-microencapsulated fuel pellet[J]. Journal of Nuclear Materials, 2021, 544: 152673.
[11] WEI H Y, JIAN X B, DING S R.A model of gas-induced effective expansion strain for porous carbon materials in irradiation environments[J]. Journal of Nuclear Materials, 2019, 515: 338-353.
[12] 陈晨, 邵宗义, 孟莹, 等. 集成学习方法在碳化硅陶瓷微封装燃料芯块烧结工艺优化中的应用研究[J]. 原子能科学技术, 2023, 57(S1): 157-165.
CHEN Chen, SHAO Zongyi, MENG Ying, et al.Application of ensemble machine learning method to sintering process optimization of SiC-ceramic microencapsulated fuel pellets[J]. Atomic Energy Science and Technology, 2023, 57(S1): 157-165.
[13] 何宗倍, 陈江善, 曾强, 等. TRISO微球涂层的微结构与力学性能研究[J]. 核动力工程, 2023, 44(3): 121-126.
HE Zongbei, CHEN Jiangshan, ZENG Qiang, et al.Study on the microstructure and mechanical properties of TRISO microsphere[J]. Nuclear Power Engineering, 2023, 44(3): 121-126.
[14] ACHIAGA B, BAREA R, CANDELA N.Determinación del criterio de rotura de Drucker-Prager de compactos en verde de α-SiC[J]. Boletín de la Sociedad Española de Cerámica y Vidrio, 2016, 55(3): 114-120.
ACHIAGA B, BAREA R, CANDELA N.Determination of Drucker-Prager failure criterion of α-SiC green compacts[J]. Journal of the Spanish Ceramic and Glass Society, 2016, 55(3): 114-120.
[15] PEDREGOSA F, VAROQUAUX G, GRAMFORT A, et al.Scikit-learn: machine learning in Python[J]. Jounal of Machine Learning Reseach, 2011, 12: 2825-2830.
[16] SHAHRIARI B, SWERSKY K, WANG Z Y, et al.Taking the human out of the loop: a review of Bayesian optimization[J]. Proceedings of The IEEE, 2016, 104(1): 148-175.
