Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
권호 표지
DOI QR코드

DOI QR Code

Study on multi-objective optimization method for radiation shield design of nuclear reactors

  • Yao Wu (Nuclear Power Institute of China) ;
  • Bin Liu (Key Laboratory of Low-grade Energy Utilization Technologies and Systems (Chongqing University), Ministry of Education) ;
  • Xiaowei Su (China Nuclear Power Technology Research Institute Co., Ltd) ;
  • Songqian Tang (Nuclear Power Institute of China) ;
  • Mingfei Yan (RIKEN Center for Advanced Photonics, RIKEN) ;
  • Liangming Pan (Key Laboratory of Low-grade Energy Utilization Technologies and Systems (Chongqing University), Ministry of Education)
  • Received : 2023.04.12
  • Accepted : 2023.10.21
  • Published : 2024.02.25
Download PDF
⟨ Previous Next ⟩

Abstract

The optimization design problem of nuclear reactor radiation shield is a typical multi-objective optimization problem with almost 10 sub-objectives and the sub-objectives are always demanded to be under tolerable limits. In this paper, a design method combining multi-objective optimization algorithms with paralleling discrete ordinate transportation code is developed and applied to shield design of the Savannah nuclear reactor. Three approaches are studied for light-weighted and compact design of radiation shield. Comparing with directly optimization with 10 objectives and the single-objective optimization, the approach by setting sub-objectives representing weight and volume as optimization objectives while treating other sub-objectives as constraints has the best performance, which is more suitable to reactor shield design.

Keywords

References

  1. A. Yamaji, K. Sako, Shield design to obtain compact marine reactor, J. Nucl. Sci. Technol. 31 (6) (1994) 510-520. 
  2. M.S. El-Genk, Deployment history and design considerations for space reactor power systems, Acta Astronaut. 64 (2009) 833-849. 
  3. Handbook of Nuclear Engineering, first ed., Springer, Boston, MA, USA, 2010. 
  4. Huasi Hu, Qunshu Wang, Juan Qin, et al., Study on composite material for mixed neutron and gamma-rays shielding, IEEE Trans. on Nuclear Science 55 (4) (2008) 2376-2384. 
  5. Cai Yao, Huasi Hu, Shuangying Lu, et al., Optimization for radiation shielding material aiming at compactness, lightweight, and low activation for a vehicle-mounted accelerator-driven D-T neutron source, Appl. Radiat. Isot. 135 (1) (2018) 147-154. 
  6. Zhenping Chen, Zhenyu Zhang, Jinsen Xie, et al., Metaheuristic optimization method for compact reactor radiation shielding design based on genetic algorithm, Ann. Nucl. Energy 134 (1) (2019) 318-329. 
  7. Z.P. Chen, Z.Y. Zhang, J.S. Xie, et al., Multi-objective optimization strategies for radiation shield design with genetic algorithm, Comput. Phys. Commun. 260 (1) (2020) 1-17. 
  8. E.P. Blizard, T.V. Blosser, R.M. Freestone Jr., The Radiation Leakage Survey of the Shield of the Nuclear Ship Savannah, Oak Ridge National Laboratory, 1962. ORNL-3336. 
  9. Y.P. Wang, Y.Q. Zhen, L.F. Xu, L. Cao, Necp-Hydra, A high performance parallel SN code for core analysis and shield calculation, Nucl. Eng. Des. 366 (1) (2020) 110711-110718. 
  10. J.E. White, D.T. Ingersoll, R.Q. Wright, Production and Testing of the Revised VITAMIN-B6 Fine Group and the BUGLE-96 Broad Group Neutron/photon Cross Section Libraries Derived from ENDF/B-BI.3 Nuclear Data, Revision 1, NUREG/CR-6214, 1996. ORNL-6795/R1. 
  11. X-5 Monte Carlo Team, MCNP-A General N-Particle Transport Code, Los Alamos National Laboratory, Santa Fe, NM, 2003. 
  12. M. Zhou, S.D. Sun, Genetic Algorithms: Theory and Applications, National Industrial Press, Beijing, 1999. 
  13. Bin Liu, Huanwen Lv, Li Lan, Xiaoming Chai, et al., Multi-objective optimization design of radiation shadow shield for space nuclear power with genetic algorithm, Front. Energy Res. 1 (10) (2022) 8009301-8009306. 
  14. M. Gen, R. Cheng, Genetic Algorithms an Engineering Optimization [M], John Wiley & Sons, New York, 2000. 
  15. Deyu Chen, Renzhong Zhang, Jihong Shen, Shiwei Gao, Study on multi-objective reactive power optimization based on genetic algorithm with adaptive weight, Power System Protection and Control 6 (38) (2010) 1-7. 
  16. Qingfu Zhang, Hui Li, MOEA/D: a multi-objective evolutionary algorithm based on decomposition, IEEE Trans. Evol. Comput. 6 (11) (2007) 712-731. 
  17. Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, T. Meyarivan, A fast and elitist multi-objective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput. 2 (6) (2002) 182-197. 
  18. Kalyanmoy Deb, Himanshu Jain, An evolutionary many-objective optimization algorithm using reference-point based non-dominated sorting approach, Part I: solving Problems with Box Constraints, IEEE Trans. Evol. Comput. 18 (4) (2014) 577-601.