Study of non-intrusive model order reduction of neutron transport problems

被引：3

作者：

Chen Wei ^{[1
]}

Yang Di ^{[1
]}

Zhang Junjie ^{[1
]}

Zhang Chunyu ^{[1
]}

Gong Helin ^{[2
]}

Xia Bangyang ^{[2
]}

Quan Yan ^{[2
]}

Wang Lianjie ^{[2
]}

机构：

[1] Sun Yat Sen Univ, Sino French Inst Nucl Engn & Technol, Zhuhai 519082, Peoples R China

[2] Sci & Technol Reactor Syst Design Technol Lab, Chengdu 610041, Peoples R China

来源：

ANNALS OF NUCLEAR ENERGY | 2021年 / 162卷

关键词：

Model order reduction; Neutron diffusion; Proper orthogonal decomposition; Gaussian process regression;

D O I：

10.1016/j.anucene.2021.108495

中图分类号：

TL [原子能技术]; O571 [原子核物理学];

学科分类号：

0827 ; 082701 ;

摘要：

Model order reduction is one important technique to speed up the solution of large-scale problems. Two model order reduction techniques, i.e., the proper orthogonal decomposition with interpolation and the Gaussian process regression, are investigated to compute the neutron flux and the effective multiplication factor. Two reduced order models are constructed and it is shown that a speedup of 3 similar to 4 orders of magnitude is achieved with guaranteed accuracy. The Gaussian process regression model can predict the mean value of keff and more importantly, the uncertainty of the predicted value can be analyzed as well. (C) 2021 Elsevier Ltd. All rights reserved.

引用

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