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Data-Driven Approach for Investigation of Irradiation Hardening Behavior of RAFM Steel

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Knowledge Science, Engineering and Management (KSEM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13369))

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Abstract

Knowledge reasoning plays an important role in applications such as the human relation web and semantic search. However, how to use this method to solve materials problems is still a challenge. Defects and damage induced by neutron irradiation significantly affect the service performance of materials. Reduced Activation Ferritic/Martensitic (RAFM) steel is a very promising candidate for application in fusion reactor cladding. Understanding irradiation hardening effects in RAFM steel is one of the critical issues. Some experimental data of RAFM steel under irradiation are collected to construct a data set. The relationship between yield strength variation after irradiation and elements and irradiation conditions is trained by the machine learning method. The influence of irradiation condition and alloy elements on the hardening behavior of RAFM steel was explored, and some optimal alloy elements composition was also recommended. This work will give some direction for RAFM steel research.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant No. U1867217, Youth Innovation Promotion Association CAS, and Key Research Program of Frontier Sciences, CAS, Grant No. ZDBS-LY-7025.

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Authors and Affiliations

  1. Computer Network Information Center, Chinese Academy of Sciences, Beijing, 100083, China

    Zongguo Wang, Ziyi Chen, Meng Wan, Jue Wang & Yangang Wang

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Zongguo Wang, Jue Wang & Yangang Wang

  3. China Institute of Atomic Energy, Beijing, 102413, China

    Xinfu He & Han Cao

  4. University of Wisconsin-Madison, Madison, WI, 53706, USA

    Yuedong Cui

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  1. Zongguo Wang
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Correspondence to Zongguo Wang .

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Editors and Affiliations

  1. Télécom Paris, Paris, France

    Gerard Memmi

  2. Purdue University, West Lafayette, IN, USA

    Baijian Yang

  3. Shanghai Jiao Tong University, Shanghai, Shanghai, China

    Linghe Kong

  4. Nanyang Technological University, Singapore, Singapore

    Tianwei Zhang

  5. Texas A&M University – Commerce, Commerce, TX, USA

    Meikang Qiu

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Wang, Z. et al. (2022). Data-Driven Approach for Investigation of Irradiation Hardening Behavior of RAFM Steel. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13369. Springer, Cham. https://doi.org/10.1007/978-3-031-10986-7_10

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  • DOI: https://doi.org/10.1007/978-3-031-10986-7_10

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-10986-7

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