An assembly-level neutronic calculation method based on LightGBM algorithm

被引:27
|
作者
Cai, Jiejin [1 ]
Li, Xuezhong [1 ]
Tan, Zhixiong [1 ]
Peng, Sitao [2 ]
机构
[1] South China Univ Technol, Sch Elect Power, 381 Wushan Rd, Guangzhou 510640, Guangdong, Peoples R China
[2] China Nucl Power Technol Res Inst, Shenzhen 518000, Guangdong, Peoples R China
基金
中国国家自然科学基金;
关键词
Artificial intelligence; Machine learning; Neutronic calculation; Assembly-level; LightGBM algorithm; FUEL-MANAGEMENT OPTIMIZATION; CORE; REACTORS;
D O I
10.1016/j.anucene.2020.107871
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
In this paper, a novel assembly-level neutronic calculation method based on a machine learning technique, the LightGBM algorithm, has been proposed. The calculating model based on LightGBM algorithm is established to calculate the assembly-level neutronic parameters, including Infinite Multiplication Factor 'Kinf', Power Peak Factor 'PPF', and Burnup 'B', which are normally calculated by exactly solving the neutron transport equations. The proposed method was first applied to assemblies cases of different sizes varying from 3 x 3, 5 x 5, 7 x 7, 9 x 9, 14 x 14 and 17 x 17 to calculate Kinf, and compared with other five Machine Learning algorithms. The results show that the proposed method based on LightGBM Algorithm has the most stable and decent performance in forecasting Infinite Multiplication Factor 'Kiln'. Therefore, LightGBM was directly implemented to calculate other neutronic calculations for a 17 x 17 assembly. The overall precision is also satisfactory. The main distribution range of errors of Kinf is [ -0.003, +0.0084], of PPF is [-0.0009, +0.0153] and of Burnup (MWD/KgU) is [ -0.0628, +0.2552]. Overall, the new proposed method will be promising in some certain application scenarios especially during the Loading Pattern (LP) optimization. (C) 2020 Elsevier Ltd. All rights reserved.
引用
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页数:10
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