The pore aggregation characterized by spatial statistics methods and its effect on the damage behavior based on the configurational forces of the M-integral in MOX fuel
Source abstract
Abstract
MOX fuel is one of the most widely used nuclear fuels in nuclear power plants. Due to the inhomogeneity of Pu, MOX fuel is prone to the formation of Pu-rich agglomerates and the occurrence of pore aggregations during irradiation. This paper aims to obtain microstructural information of the fuel using spatial statistics methods and investigate the impact of pore aggregation on MOX fuel damage based on the configurational forces of the M -integral. Firstly, the pore aggregation model is established by random sequence adsorption method twice, and the relationship between fuel model size and pore diameter is determined by point pattern criteria based on spatial statistics methods. Secondly, through spatial statistics methods, the reliability of the pore aggregation model to characterize different aggregation levels is verified. Simultaneously, it is confirmed that the M-integral delineating the global state is a better indicator of fuel damage compared to physical quantities that delineate the local state. Finally, a damage assessment model based on the M -integral is established by the basic characteristics of MOX fuel. The effects of the volume fraction of Pu-rich agglomerates, the Pu weight fraction within Pu-rich agglomerates and the radial position of the fuel pellet on the fuel damage are analyzed. It is concluded that the concentrated distribution of Pu promotes the damage of MOX fuel. The MOX fuel pellet center suffers the most damage. This study has proposed a damage evaluation method for MOX fuel microstructure with pore aggregation and established a relationship between the spatial distribution of fuel pores and fuel damage.
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Research context
OpenAlex topics: Nuclear Materials and Properties · High Temperature Alloys and Creep · Mineral Processing and Grinding
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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