Attempts on representing sink strengths with machine learning formulations and the long-term role of crystalline interfaces in the development of irradiation-induced bubbles

JOURNAL OF NUCLEAR MATERIALS

卷:544

Article

Luo, Jing¹  Xin, Yong²  Zhou, Zhengcheng¹  Zhu, Yichao^1,3,4  Guo, Xu^1,3,4 

[1] Dalian Univ Technol, Dept Engn Mech, Dalian 116023, Peoples R China

[2] Nucl Power Inst China, Sci & Technol Reactor Syst Design Technol Lab, Chengdu 610213, Peoples R China

[3] Dalian Univ Technol, State Key Lab Struct Anal Ind Equipment, Dalian, Peoples R China

[4] Dalian Univ Technol, Int Res Ctr Computat Mech, Dalian, Peoples R China

关键词: Rate equations;    Sink strength;    Crystalline interfaces;    Partial sinks;    Machine learning;   

DOI  :  10.1016/j.jnucmat.2020.152676

来源: Elsevier

【 摘 要 】

The present article addresses an early-stage attempt on replacing the analyticity-based sink strength terms in rate equations by means of a surrogate representation. Here we emphasise, in the context of multiscale modelling, a combinative use of machine learning with scale analysis, through which a set of fine-resolution problems of partial differential equations describing the local sink behaviour, can be asymptotically sorted out from the mean-field kinetics. Hence the training of machine learning formulation is restrictively oriented, that is, to express the local and already identified, but analytically unavailable nonlinear functional relationships between the sink strengths and other local continuum field quantities. Besides, the normally faster diffusion mechanisms on crystalline interfaces are particularly modelled by locally planar rate equations, and their linkages with rate equations for bulk diffusion are formulated by imposing an irregular boundary condition where point defect fluxes are discontinuous across the interfaces. Thus the distinctive role of crystalline interfaces as partial sinks and quick diffusion channels can be investigated. Methodologicalwise, the present treatment is also applicable for studying more complicated situation of long-term sink behaviour observed in irradiated materials. (C) 2020 Elsevier B.V. All rights reserved.

【 授权许可】

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