基于有限元和机器学习的全陶瓷微封装燃料热压烧结应力分析

doi:10.19976/j.cnki.43-1448/TF.2024108

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Abstract 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.

Key words： accident tolerant fuel full ceramic microencapsulated fuel hot press sintering finite element method machine learning

Received: 11 December 2024 Published: 13 October 2025

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	HE Zongbei
	OUYANG Han
	DU Zirui
	ZENG Qiang
	GAO Xinrui
	GUAN Kang

Cite this article:

HE Zongbei,OUYANG Han,DU Zirui, et al. Hot press sintering stress analysis of full ceramic microencapsulated fuel based on finite element method and machine learning[J]. Materials Science and Engineering of Powder Metallurgy, 2025, 30(4): 272-288.

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http://pmbjb.csu.edu.cn/EN/10.19976/j.cnki.43-1448/TF.2024108 OR http://pmbjb.csu.edu.cn/EN/Y2025/V30/I4/272

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