Skip to main content
SpringerLink
Log in
Book cover

International Conference on Knowledge Science, Engineering and Management

KSEM 2022: Knowledge Science, Engineering and Management pp 117–127Cite as

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

  • Conference paper
  • First Online:

  • 1694 Accesses

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

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.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Qiu, M., Xue, C., Shao, Z., Sha, E.: Energy minimization with soft real-time and DVS for uniprocessor and multiprocessor embedded systems. In: IEEE DATE Conference, pp. 1–6 (2007)

    Google Scholar 

  2. Qiu, M., Guo, M., Liu, M., et al.: Loop scheduling and bank type assignment for heterogeneous multi-bank memory. J. Parallel Distrib. Comput. 69(6), 546–558 (2009)

    Article  Google Scholar 

  3. Qiu, M., Xue, C., Shao, Z., et al.: Efficient algorithm of energy minimization for heterogeneous wireless sensor network. In: IEEE EUC, pp. 25–34 (2006)

    Google Scholar 

  4. Lu, Z., Wang, N., Wu, J., Qiu, M.: IoTDeM: an IoT Big Data-oriented MapReduce performance prediction extended model in multiple edge clouds. JPDC 118, 316–327 (2018)

    Google Scholar 

  5. Qiu, M., Liu, J., Li, J., et al.: A novel energy-aware fault tolerance mechanism for wireless sensor networks. In: IEEE/ACM International Conference on GCC (2011)

    Google Scholar 

  6. Niu, J., Gao, Y., Qiu, M., Ming, Z.: Selecting proper wireless network interfaces for user experience enhancement with guaranteed probability. JPDC 72(12), 1565–1575 (2012)

    Google Scholar 

  7. Liu, M., Zhang, S., Fan, Z., Qiu, M.: “H state estimation for discrete-time chaotic systems based on a unified model. IEEE Trans. Syst. Man Cybern. (B), 42(4), 1053–1063 (2012)

    Google Scholar 

  8. Qiu, M., Li, H., Sha, E.: Heterogeneous real-time embedded software optimization considering hardware platform. In: ACM Symposium on Applied Computing, pp. 1637–1641 (2009)

    Google Scholar 

  9. Qiu, M., Sha, E., Liu, M., et al.: Energy minimization with loop fusion and multi-functional-unit scheduling for multidimensional DSP. JPDC 68(4), 443–455 (2008)

    MATH  Google Scholar 

  10. Qiu, H., Qiu, M., Liu, M., Memmi, G.: Secure health data sharing for medical cyber-physical systems for the healthcare 4.0. IEEE J. Biomed. Health Inform. 24(9), 2499–2505 (2020)

    Article  Google Scholar 

  11. Qiu, M., Gai, K., Xiong, Z.: Privacy-preserving wireless communications using bipartite matching in social big data. FGCS 87, 772–781 (2018)

    Article  Google Scholar 

  12. Shao, Z., Xue, C., Zhuge, Q., et al.: Security protection and checking for embedded system integration against buffer overflow attacks via hardware/software. IEEE Trans. Comput. 55(4), 443–453 (2006)

    Article  Google Scholar 

  13. Qiu, H., Qiu, M., Lu, Z.: Selective encryption on ECG data in body sensor network based on supervised machine learning. Inf. Fusion 55, 59–67 (2020)

    Article  Google Scholar 

  14. Qiu, L., Gai, K., Qiu, M.: Optimal big data sharing approach for tele-health in cloud computing. In: IEEE SmartCloud, pp. 184–189 (2016)

    Google Scholar 

  15. Wu, G., Zhang, H., Qiu, M., et al.: A decentralized approach for mining event correlations in distributed system monitoring. JPDC 73(3), 330–340 (2013)

    MATH  Google Scholar 

  16. Qiu, M., Cao, D., Su, H., Gai, K.: Data transfer minimization for financial derivative pricing using Monte Carlo simulation with GPU in 5G. Int. J. of Comm. Sys. 29(16), 2364–2374 (2016)

    Article  Google Scholar 

  17. Wang, J., Qiu, M., Guo, B.: Enabling real-time information service on telehealth system over cloud-based big data platform. J. Syst. Architect. 72, 69–79 (2017)

    Article  Google Scholar 

  18. Tsuzuki, K., Sato, M., Kawashima, H., et al.: Recent activities on the compatibility of the ferritic steel wall with the plasma in the JFT-2M tokamak. J. Nucl. Mater. 307–311, 1386–1390 (2002)

    Article  Google Scholar 

  19. Salavy, J.-F., Aiello, G., Aubert, P., et al.: Ferritic-martensitic steel test blanket modules: status and future needs for design criteria requirements and fabrication validation. J. Nucl. Mater. 386–388, 922–926 (2009)

    Article  Google Scholar 

  20. Zhan, D.-P., Qiu, G.-X., Li, C.-S., Qi, M., Jiang, Z.-H., Zhang, H.-S.: Effects of yttrium and zirconium additions on inclusions and mechanical properties of a reduced activation ferritic/martensitic steel. J. Iron. Steel Res. Int. 27(2), 197–207 (2019). https://doi.org/10.1007/s42243-019-00332-9

    Article  Google Scholar 

  21. Zinkle, S.J.: Fusion materials science: Overview of challenges and recent progress. Phys. Plasmas 12, 058101 (2005)

    Article  Google Scholar 

  22. Muroga, T., Gasparotto, M., Zinkle, S.J.: Overview of materials research for fusion reactors. Fusion Eng. Des. 61–62, 13–25 (2002)

    Article  Google Scholar 

  23. van der Schaaf, B., Gelles, D.S., et al.: Progress and critical issues of reduced activation ferritic/martensitic steel Development. J. Nucl. Mater. 283–287, 52–59 (2000)

    Article  Google Scholar 

  24. Jitsukawa, S., Tamura, M., et al.: Development of an extensive database of mechanical and physical properties for reduced-activation martensitic steel F82H. J. Nucl. Mater. 307–311, 179–186 (2002)

    Article  Google Scholar 

  25. Qiu, G., Zhan, D., Li, C., Qi, M., Jiang, Z., Zhang, H.: Effect of Y/Zr ratio on inclusions and mechanical properties of 9Cr-RAFM steel fabricated by vacuum melting. J. Mater. Eng. Perform. 28(2), 1067–1076 (2019). https://doi.org/10.1007/s11665-018-3838-0

    Article  Google Scholar 

  26. He Pei, Yao Wei-zhi, YU Jian-ming, Zhang Xiang-dong. “Evaluation of Irradiation Properties for Fusion Structural Materials”, Journal of Materials Engineering, 46(6), 19–26 (2018)

    Google Scholar 

  27. Gaganidze, E., Aktaa, J.: Assessment of neutron irradiation effects on RAFM steels. Fusion Eng. Des. 88, 118–128 (2013)

    Article  Google Scholar 

  28. Mansur, L.K., Rowcliffe, A.F., Nanstad, R.K., et al.: Materials needs for fusion, generation IV fission reactors and spallation neutron sources-similarities and differences. J. Nucl. Mater. 329–333, 166–172 (2004)

    Article  Google Scholar 

  29. Cottrell, G.A., Baker, L.J.: Structural materials for fusion and spallation sources. J. Nucl. Mater. 318, 260–266 (2003)

    Article  Google Scholar 

  30. Gaganidze, E., Dafferner, B., et al.: Irradiation programme HFR phase IIb-SPICE. Impact testing on up to 16.3 dpa irradiated RAFM steels. Final report for task TW2-TTMS 001b-D05, 7371, 0947–8620 (2008)

    Google Scholar 

  31. Qiu, H., Qiu, M., Lu, Z., Memmi, G.: An efficient key distribution system for data fusion in V2X heterogeneous networks. Inf. Fusion 50(1), 212–220 (2019)

    Article  Google Scholar 

  32. Qiu, H., Zheng, Q., et al.: Topological graph convolutional network-based urban traffic flow and density prediction. IEEE TITS 22(7), 4560–4569 (2021)

    Google Scholar 

  33. Li, Y., Song, Y., et al.: Intelligent fault diagnosis by fusing domain adversarial training and maximum mean discrepancy via ensemble learn. IEEE TII 17(4), 2833–2841 (2021)

    Google Scholar 

  34. Hu, F., Lakdawala, S., et al.: Low-power, intelligent sensor hardware interface for medical data preprocessing. IEEE Trans. Inf. Tech. Biomed. 13(4), 656–663 (2009)

    Article  Google Scholar 

  35. Lu, R., Jin, X., Zhang, S., Qiu, M., Wu, X.: A study on big-knowledge and its engineering issues. IEEE Trans. Knowl. Data Eng. 31(9), 1630–1644 (2019)

    Article  Google Scholar 

  36. Qiu, M., Chen, Z., Ming, Z., Qiu, X., Niu, J.: Energy-aware data allocation with hybrid memory for mobile cloud systems. IEEE Syst. J. 11(2), 813–822 (2017)

    Article  Google Scholar 

  37. Yao, T., Wang, J. Meng Wan, et al.: VenusAI: an artificial intelligence platform for scientific discovery on supercomputers. J. Syst. Archit. 128, 102550 (2022)

    Google Scholar 

  38. Porollo, S.I., Dvoriashin, A.M., et al.: The microstructure and tensile properties of Fe–Cr alloys after neutron irradiation at 400 C to 5.5–7.1 dpa. J. Nucl. Mater., 256(2–3): 247–253 (1998)

    Google Scholar 

Download references

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.

Author information

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

Authors
  1. Zongguo Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ziyi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xinfu He
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Han Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yuedong Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Meng Wan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jue Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yangang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zongguo Wang .

Editor information

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

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

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

Download citation

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

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-10985-0

  • Online ISBN: 978-3-031-10986-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation