基于机器学习的RAFM钢中子辐照脆化预测模型研究

doi:10.11896/cldb.22010142

材料导报

2023, Vol. 37

Issue (1): 22010142-7 https://doi.org/10.11896/cldb.22010142

金属与金属基复合材料

基于机器学习的RAFM钢中子辐照脆化预测模型研究

李孝晨^1,2, 丁文艺¹, 朱霄汉^1,2, 郑明杰^1,2,*

1 中国科学院合肥物质科学研究院,合肥 230031
2 中国科学技术大学,合肥 230026

Research on Prediction Model of Neutron Irradiation Embrittlement of RAFM Steels Based on Machine Learning

LI Xiaochen^1,2, DING Wenyi¹, ZHU Xiaohan^1,2, ZHENG Mingjie^1,2,*

1 Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
2 University of Science and Technology of China, Hefei 230026, China

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摘要构建低活化铁素体/马氏体(RAFM)钢的中子辐照脆化预测模型对聚变反应堆的安全运行和优化设计新型RAFM钢具有十分重要的意义。本研究基于收集的RAFM钢中子辐照数据集,采用相关性筛选、递归消除方法识别出影响RAFM钢中子辐照条件下韧脆转变温度(DBTT)的关键特征变量。利用筛选的关键特征变量,构建了具有良好预测能力的RAFM钢中子辐照DBTT预测模型。为进一步实现中子辐照条件下韧脆转变温度变化(ΔDBTT)的预测,首先构建了RAFM钢未辐照DBTT预测模型,然后将辐照前后DBTT预测模型相结合构建了RAFM钢中子辐照ΔDBTT预测模型。通过将模型预测的ΔDBTT与文献收集的数据进行对比发现,该模型具备较好的预测能力。

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	李孝晨
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	郑明杰

关键词: 机器学习 RAFM钢辐照脆化韧脆转变温度

Abstract: Building the neutron irradiation embrittlement prediction model of reduced activation ferritic/martensitic (RAFM) steels is of great significance for the safe operation of the fusion reactor and the optimal design of new RAFM steels. In the present work, based on the collected neutron irradiation dataset of RAFM steels, the key features affecting the ductile-brittle transition temperature (DBTT) of RAFM steels under neutron irradiation are identified by correlation screening and recursive elimination methods. Using the selected key features, the prediction model for DBTT of neutron-irradiated RAFM steels with good prediction ability is constructed. In order to further predict the ductile-brittle transition temperature shift (ΔDBTT) under neutron irradiation, the prediction model for DBTT of un-irradiated RAFM steels is constructed. The prediction model for ΔDBTT of neutron-irradiated RAFM steels is constructed by combining the prediction models before and after irradiation. By comparing the ΔDBTT predicted by the model with the data collected from the related experimental literatures, the results indicate that this prediction model has high precision and reliability.

Key words: machine learning RAFM steel irradiation embrittlement DBTT

出版日期: 2023-01-10 发布日期: 2023-01-31

ZTFLH:

TG113.25

基金资助: 国家重点研发计划项目(2018YFE0307104);国家自然科学基金(11632001);中国科学院合肥物质科学研究院院长基金国际合作探索项目(2021YZGH05);中国科学院特别交流计划A类(E2AAAI13)

通讯作者: * 郑明杰,中国科学院合肥物质科学研究院研究员,中科院“百人计划”,博士研究生导师。2003年获山东师范大学物理学学士学位,2006年获北京大学光学硕士学位,2009年获中国香港中文大学物理学博士学位。长期从事先进反应堆材料辐照损伤多尺度模拟与抗辐照材料设计研究。已在Physical Review Letter、Physical Review B、Materials & Design、Journal of Nuclear Materials等重要期刊上发表论文30余篇。mingjie.zheng@inest.cas.cn

作者简介: 李孝晨,2019年6月于中国科学院大学获得工学硕士学位。现为中国科学技术大学研究生院科学岛分院博士研究生,在郑明杰研究员的指导下进行研究。主要从事基于机器学习的低活化铁素体/马氏体钢及高熵合金研究。

引用本文:

李孝晨, 丁文艺, 朱霄汉, 郑明杰. 基于机器学习的RAFM钢中子辐照脆化预测模型研究[J]. 材料导报, 2023, 37(1): 22010142-7.
LI Xiaochen, DING Wenyi, ZHU Xiaohan, ZHENG Mingjie. Research on Prediction Model of Neutron Irradiation Embrittlement of RAFM Steels Based on Machine Learning. Materials Reports, 2023, 37(1): 22010142-7.

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http://www.mater-rep.com/CN/10.11896/cldb.22010142 或 http://www.mater-rep.com/CN/Y2023/V37/I1/22010142

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