Predicting the Irradiation Swelling of Austenitic and Ferritic/Martensitic Steels, Based on the Coupled Model of Machine Learning and Rate Theory

doi:10.3390/met12040651

	Predicting the Irradiation Swelling of Austenitic and Ferritic/Martensitic Steels, Based on the Coupled Model of Machine Learning and Rate Theory
	Zhu, Xiaohan 1,2; Li, Xiaochen 1,2; Zheng, Mingjie 1,2
刊名	METALS
	2022-04-01
卷号	12
关键词	austenitic and ferritic/martensitic steels irradiation swelling rate theory machine learning
DOI	10.3390/met12040651
通讯作者	Zheng, Mingjie(mingjie.zheng@inest.cas.cn)
英文摘要	As nuclear structural materials, austenitic and ferritic/martensitic (F/M) steels will face inevitable irradiation swelling when fulfilling a role in nuclear reactors, especially under high-dose irradiation. For this work, a coupled machine learning rate theory (ML-RT) model for the swelling of austenitic and F/M steels was developed. In this model, ML was introduced to predict the steady-state irradiation swelling onset dose (D-onset), while the improved RT was developed to simulate the swelling behavior after the incubation period. More than 200 series of data on the D-onset of different structures of steel were collected for the ML prediction. The coefficient of determination (R) of the results in ML is more than 0.9. In the RT, the evolutions of the dislocation loop and void were described and calculated rather than using the fitting parameters. Cascade efficiency was employed to describe the cascade process. The coupled ML-RT model was verified with the swelling data from neutron irradiation experiments for various steels. The theoretical results of the swelling peak temperatures and swelling behavior are more accurate and reasonable, compared with those from the previous RT model. Using the ML-RT model, the swelling performance of CLAM steel under neutron irradiation of up to 180 dpa was predicted. The differences between the swelling performance of austenitic steels and F/M steels were analyzed and the differences were mainly associated with the bias. These results will be helpful for evaluating the neutron irradiation swelling behavior of candidate structural materials.
资助项目	National Natural Science Foundation of China[11632001] ; National Natural Science Foundation of China[11675209] ; Foundation of the President of the Hefei Institutes of Physical Science, Chinese Academy of Sciences[2021YZGH05]
WOS关键词	FE-CR ALLOYS ; DEFECT PRODUCTION ; DOSE-RATE ; MIGRATION ; EVOLUTION ; DAMAGE ; BCC ; MICROSTRUCTURE ; COALESCENCE ; RESISTANCE
WOS研究方向	Materials Science ; Metallurgy & Metallurgical Engineering
语种	英语
出版者	MDPI
WOS记录号	WOS:000786218100001
资助机构	National Natural Science Foundation of China ; Foundation of the President of the Hefei Institutes of Physical Science, Chinese Academy of Sciences
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/128627]
专题	中国科学院合肥物质科学研究院
通讯作者	Zheng, Mingjie
作者单位	1.Chinese Acad Sci, Inst Nucl Energy Safety Technol, Hefei Inst Phys Sci, Hefei 230031, Peoples R China 2.Univ Sci & Technol China, Hefei 230026, Peoples R China
推荐引用方式 GB/T 7714	Zhu, Xiaohan,Li, Xiaochen,Zheng, Mingjie. Predicting the Irradiation Swelling of Austenitic and Ferritic/Martensitic Steels, Based on the Coupled Model of Machine Learning and Rate Theory[J]. METALS,2022,12.
APA	Zhu, Xiaohan,Li, Xiaochen,&Zheng, Mingjie.(2022).Predicting the Irradiation Swelling of Austenitic and Ferritic/Martensitic Steels, Based on the Coupled Model of Machine Learning and Rate Theory.METALS,12.
MLA	Zhu, Xiaohan,et al."Predicting the Irradiation Swelling of Austenitic and Ferritic/Martensitic Steels, Based on the Coupled Model of Machine Learning and Rate Theory".METALS 12(2022).