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A Preliminary Study on the CLAM Steel Composition Optimization Based on Extreme Learning Machine

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Abstract

Material problem is one of the key issues for the realization of fusion energy. Reduced Activation Ferritic/Martensitic steels have been considered to be the primary candidate structural material for fusion DEMO reactors and the first fusion power plants. China low activation martensitic steel (CLAM) has been chosen as the primary structure material in the designs of FDS series PbLi blankets for fusion reactors, CN helium cooled ceramic breeder test blanket module for ITER, liquid blanket of China fusion engineering test reactor (CFETR). In this work, optimization of CLAM steel composition was performed based on extreme learning machine method. The results showed that CLAM steel with Ta content of 0.18–0.20 % had better tensile property at the temperature of 350–550 °C, which provided references for the optimization of CLAM steel composition.

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Acknowledgments

The authors would like to show their great appreciation to other members in FDS Team for their supports and contributions to this research. This work was supported by the ITER 973 Program (2013GB108005), the Special Program for Informatization of the Chinese Academy of Sciences (XXH12504-1-09) and the young foundation of Huaibei Normal University (2014xq013).

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Authors and Affiliations

  1. University of Science and Technology of China, Hefei, 230027, Anhui, China

    Longfeng Shen & Xiangwei Zhai

  2. Key Laboratory of Neutronics and Radiation Safety, Institute of Nuclear Energy Safety Technology, Chinese Academy of Sciences, Hefei, 230031, Anhui, China

    Longfeng Shen, Xiangwei Zhai, Chaobin Chen, Chunjing Li & Fang Wang

  3. Huaibei Normal University, Huaibei, 235000, Anhui, China

    Longfeng Shen

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  1. Longfeng Shen
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  2. Xiangwei Zhai
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  3. Chaobin Chen
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  4. Chunjing Li
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  5. Fang Wang
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Correspondence to Fang Wang.

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Shen, L., Zhai, X., Chen, C. et al. A Preliminary Study on the CLAM Steel Composition Optimization Based on Extreme Learning Machine. J Fusion Energ 34, 1071–1076 (2015). https://doi.org/10.1007/s10894-015-9912-9

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  • DOI: https://doi.org/10.1007/s10894-015-9912-9

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