Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
권호 표지
DOI QR코드

DOI QR Code

An intelligent optimization method for the HCSB blanket based on an improved multi-objective NSGA-III algorithm and an adaptive BP neural network

  • Wen Zhou (Department of Nuclear Engineering and Management, School of Engineering, University of Tokyo) ;
  • Guomin Sun (Key Laboratory of Neutronics and Radiation Safety, Hefei Institutes of Physical Science, Chinese Academy of Sciences) ;
  • Shuichiro Miwa (Department of Nuclear Engineering and Management, School of Engineering, University of Tokyo) ;
  • Zihui Yang (Key Laboratory of Neutronics and Radiation Safety, Hefei Institutes of Physical Science, Chinese Academy of Sciences) ;
  • Zhuang Li (Key Laboratory of Neutronics and Radiation Safety, Hefei Institutes of Physical Science, Chinese Academy of Sciences) ;
  • Di Zhang (Key Laboratory of Neutronics and Radiation Safety, Hefei Institutes of Physical Science, Chinese Academy of Sciences) ;
  • Jianye Wang (Key Laboratory of Neutronics and Radiation Safety, Hefei Institutes of Physical Science, Chinese Academy of Sciences)
  • Received : 2023.01.09
  • Accepted : 2023.05.19
  • Published : 2023.09.25
Download PDF
⟨ Previous Next ⟩

Abstract

To improve the performance of blanket: maximizing the tritium breeding rate (TBR) for tritium self-sufficiency, and minimizing the Dose of backplate for radiation protection, most previous studies are based on manual corrections to adjust the blanket structure to achieve optimization design, but it is difficult to find an optimal structure and tends to be trapped by local optimizations as it involves multiphysics field design, which is also inefficient and time-consuming process. The artificial intelligence (AI) maybe is a potential method for the optimization design of the blanket. So, this paper aims to develop an intelligent optimization method based on an improved multi-objective NSGA-III algorithm and an adaptive BP neural network to solve these problems mentioned above. This method has been applied on optimizing the radial arrangement of a conceptual design of CFETR HCSB blanket. Finally, a series of optimal radial arrangements are obtained under the constraints that the temperature of each component of the blanket does not exceed the limit and the radial length remains unchanged, the efficiency of the blanket optimization design is significantly improved. This study will provide a clue and inspiration for the application of artificial intelligence technology in the optimization design of blanket.

Keywords

Acknowledgement

This work was funded by the National Key Research and Development Program of China (2019YFE0191700) and by the Natural Science Foundation of Anhui Province (2008085MA23).

References

  1. Y.C. Wu, S. S, ahin, Fusion energy production, Compr. Energy Syst. 13 (2018) 538-589, https://doi.org/10.1016/b978-0-12-809597-3.00330-8.
  2. L.M. Giancarli, X. Bravo, S. Cho, et al., Overview of recent ITER TBM Program activities, Fusion Eng. Des. 158 (2020), 111674, https://doi.org/10.1016/j.fusengdes.2020.111674.
  3. X.H. Wu, H.B. Liao, X.Y. Wang, et al., Design optimization and analysis of CN HCCB TBM-set, Fusion Eng. Des. 136 (2018) 839-846, https://doi.org/10.1016/j.fusengdes.2018.04.018.
  4. F. Hernandez, P. Pereslavtsev, Q. Kang, et al., A new HCPB breeding blanket for  the EU DEMO: evolution, rationale and preliminary performances, Fusion Eng. Des. 124 (2017) 882-886, https://doi.org/10.1016/j.fusengdes.2017.02.008.
  5. F.A. Hernandez, F. Arbeiter, L.V. Boccaccini, et al., Overview of the HCPB  research activities in EUROfusion, IEEE Trans. Plasma Sci. 46 (2018) 2247-2261, https://doi.org/10.1109/tps.2018.2830813.
  6. F.A. Hernandez, P. Pereslavtsev, G. Zhou, et al., Consolidated design of the  HCPB breeding blanket for the pre-conceptual design phase of the EU DEMO and harmonization with the ITER HCPB TBM program, Fusion Eng. Des. 157 (2020), 111614, https://doi.org/10.1016/j.fusengdes.2020.111614.
  7. P. Pereslavtsev, U. Fischer, F. Hernandez, et al., Neutronic analyses for the optimization of the advanced HCPB breeder blanket design for DEMO, Fusion Eng. Des. 124 (2017) 910-914, https://doi.org/10.1016/j.fusengdes.2017.01.028.
  8. D. Zhang, S. Cui, J. Cheng, et al., Improving the optimization algorithm of NTCOC for application in the HCSB blanket for CFETR Phase II, Fusion Eng. Des. 135 (2018) 216-227, https://doi.org/10.1016/j.fusengdes.2018.07.027.
  9. J. Aubert, G. Aiello, C. Bachmann, et al., Optimization of the first wall for the DEMO water cooled lithium lead blanket, Fusion Eng. Des. 98 (2015) 1206-1210, https://doi.org/10.1016/j.fusengdes.2015.01.008.
  10. F. Gaudier, URANIE: the CEA/DEN uncertainty and sensitivity platform, Procedia Soc. Behav Sci. 2 (2010) 7660-7661, https://doi.org/10.1016/j.sbspro.2010.05.166.
  11. I. Antcheva, M. Ballintijn, B. Bellenot, et al., ROOT-a C++ framework for petabyte data storage, statistical analysis and visualization, Comput. Phys. Commun. 182 (2011) 1384-1385, https://doi.org/10.1016/j.cpc.2011.02.008.
  12. C.H. Noh, W. Chung, J. Lim, et al., Optimization of the outer support in the ITER lower cryostat thermal shield, Fusion Eng. Des. 103 (2016) 85-92, https://doi.org/10.1016/j.fusengdes.2015.12.014.
  13. C. Coello, C.A. Coello, A short tutorial on evolutionary multiobjective optimization, in: International Conference on Evolutionary Multi-Criterion Optimization, Springer, Berlin, Heidelberg, 2001, pp. 21-40, https://doi.org/10.1007/3-540-44719-9_2.
  14. P. Seferlis, M.C. Georgiadis (Eds.), The Integration of Process Design and Control, Elsevier, 2004, https://doi.org/10.1016/s1570-7946(04)80052-x.
  15. N. Srinivas, K. Deb, Muiltiobjective optimization using nondominated sorting in genetic algorithms, Evol. Comput. 2 (1994) 221-248, https://doi.org/10.1162/evco.1994.2.3.221.
  16. K. Deb, S. Agrawal, A. Pratap, et al., A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II, in: International Conference on Parallel Problem Solving from Nature, Springer, Berlin, Heidelberg, 2000, pp. 849-858, https://doi.org/10.1007/3-540-45356-3_83.
  17. E. Zitzler, Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications, vol. 63, Shaker, Ithaca, 1999.
  18. E. Zitzler, M. Laumanns, L. Thiele, SPEA2: improving the strength Pareto evolutionary algorithm, TIK-report. 103 (2001), https://doi.org/10.3929/ethz-a-004284029.
  19. D.W. Corne, N.R. Jerram, J.D. Knowles, et al., PESA-II: region-based selection in evolutionary multiobjective optimization, in: Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation, July 2001, pp. 283-290.
  20. K. Deb, H. Jain, An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints, IEEE Trans. Evol. Comput. 18 (4) (2013) 577-601, https://doi.org/10.1109/tevc.2013.2281535.
  21. H. Ishibuchi, R. Imada, Y. Setoguchi et al., Performance comparison of NSGA-II and NSGA-III on various many-objective test problems. In 2016 IEEE Congress on Evolutionary Computation (CEC). pp. 3045-3052. doi: 10.1109/cec.2016.7744174.
  22. G.C. Ciro, F. Dugardin, F. Yalaoui, et al., A NSGA-II and NSGA-III comparison for solving an open shop scheduling problem with resource constraints, IFAC-PapersOnLine 49 (2016) 1272-1277, https://doi.org/10.1016/j.ifacol.2016.07.690.
  23. V. Yannibelli, E. Pacini, D. Monge, et al., A comparative analysis of NSGA-II and NSGA-III for autoscaling parameter sweep experiments in the cloud, Sci. Program. 6 (2020) 1-17, https://doi.org/10.1155/2020/4653204.
  24. A. Teymourifar, A.M. Rodrigues, J.S. Ferreira, A comparison between NSGA-II and NSGA-III to solve multi-objective sectorization problems based on statistical parameter tuning, in: 2020 24th International Conference on Circuits, Systems, Communications and Computers (CSCC), IEEE, 2020, pp. 64-74, https://doi.org/10.1109/cscc49995.2020.00020.
  25. R. Storn, K. Price, Differential evolutionea simple and efficient heuristic for global optimization over continuous spaces, J. Global Optim. 11 (4) (1997) 341-359, https://doi.org/10.1023/A:1008202821328.
  26. J. Qiang, A Unified Differential Evolution Algorithm for Global Optimization.
  27. F. Ge, K. Li, Y. Han, Solving interval many-objective optimization problems by combination of NSGA-III and a local fruit fly optimization algorithm, Appl. Soft Comput. 114 (2022), 108096, https://doi.org/10.1016/j.asoc.2021.108096.
  28. G. Rudolph, Convergence analysis of canonical genetic algorithms, IEEE Trans. Neural Network. 5 (1) (1994) 96-101, https://doi.org/10.1109/72.265964.
  29. S. Das, P.N. Suganthan, Differential evolution: a survey of the state-of-the-art, IEEE Trans. Evol. Comput. 15 (1) (2010) 4-31, https://doi.org/10.1109/tevc.2010.2059031.
  30. S. Huband, P. Hingston, L. Barone, et al., A review of multiobjective test problems and a scalable test problem toolkit, IEEE Trans. Evol. Comput. 10 (5) (2006) 477-506, https://doi.org/10.1109/tevc.2005.861417.
  31. C. Audet, J. Bigeon, D. Cartier, et al., Performance indicators in multiobjective optimization, Eur. J. Oper. Res. 292 (2) (2021) 397-422, https://doi.org/10.1016/j.ejor.2020.11.016.
  32. W. Zhou, G. Sun, Z. Yang, et al., BP neural network based reconstruction method for radiation field applications, Nucl. Eng. Des. 380 (2021), 111228, https://doi.org/10.1016/j.nucengdes.2021.111228.
  33. M. Ni, C. Lian, S. Zhang, et al., Structural design and preliminary analysis of liquid leadelithium blanket for China Fusion Engineering Test Reactor, Fusion Eng. Des. 94 (2015) 61-66, https://doi.org/10.1016/j.fusengdes.2015.03.018.
  34. W. Li, W. Shi, Q. Zeng, et al., The assessment of shutdown dose rate and radioactive waste of HCSB during its replacement in CFETR, Fusion Eng. Des. 131 (2018) 15-20, https://doi.org/10.1016/j.fusengdes.2018.04.031.
  35. Z. Lv, H. Chen, C. Chen, et al., Preliminary neutronics design and analysis of helium cooled solid breeder blanket for CFETR, Fusion Eng. Des. 95 (2015) 79-83, https://doi.org/10.1016/j.fusengdes.2015.04.038.
  36. S. Cui, D. Zhang, Y. Lang, et al., A new method for improving the tritium breeding and releasing performance of China Fusion Engineering Test Reactor phase II helium-cooled ceramic breeder blanket, Int. J. Energy Res. 44 (7) (2020) 5977-5989, https://doi.org/10.1002/er.5392.
  37. Y.C. Wu, Multifunctional neutronics calculation methodology and program for nuclear design and radiation safety evaluation, Fusion Sci. Technol. 74 (4) (2018) 321-329, https://doi.org/10.1080/15361055.2018.1475162.
  38. R.A. Forrest, R. Capote, N. Otsuka, et al., FENDL-3 Library-Summary Documentation (No. INDC (NDS)-0628), International Atomic Energy Agency, 2012.
  39. K.L. Lawrence, ANSYS Workbench Tutorial Release 14, SDC publications, 2012.
  40. H. Chen, M. Li, Z. Lv, et al., Conceptual design and analysis of the helium cooled solid breeder blanket for CFETR, Fusion Eng. Des. 96 (2015) 89-94, https://doi.org/10.1016/j.fusengdes.2015.02.045.
  41. M. Moscardini, Helium Cooled Pebble Bed Test Blanket Module for a Nuclear Fusion Reactor: Thermo Mechanical Analyses of the Breeder Unit, 2013.
  42. J. Reimann, S. Hermsmeyer, Thermal conductivity of compressed ceramic breeder pebble beds, Fusion Eng. Des. 61 (2002) 345-351, https://doi.org/10.1016/s0920-3796(02)00165-5.
  43. P. Kittisuwan, Relation between Penalized Least Squares Regression and Bayesian Estimation in AWGN Based on Novel Penalty Function of Pareto Density, ICT Express, 2022, https://doi.org/10.1016/j.icte.2022.01.012.
  44. J. Li, Q. Zeng, M. Chen, J. Jiang, S. Zheng, FDS Team, Comparison analysis of 1D/2D/3D neutronics modeling for a fusion reactor, Fusion Eng. Des. 83 (10-12) (2008) 1678-1682, https://doi.org/10.1016/j.fusengdes.2008.06.051.
  45. J.C. Helton, F.J. Davis, Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems, Reliab. Eng. Syst. Saf. 81 (1) (2003) 23-69, https://doi.org/10.1016/s0951-8320(03)00058-9.
  46. L. El-Guebaly, V. Massaut, K. Tobita, et al., Evaluation of Recent Scenarios for Managing Fusion Activated Materials: Recycling and Clearance, Avoiding Disposal, UWFDM-1333, University of Wisconsin Fusion Technology Institute~ Sep, 2007.