Radiation shielding optimization design research based on bare-bones particle swarm optimization algorithm
0
조회
0
다운로드
2
인용
0
참고문헌
키워드
초록
In order to further meet the requirements of weight, volume, and dose minimization for new nuclear energy devices, the bare-bones multi-objective particle swarm optimization algorithm is used to automatically and iteratively optimize the design parameters of radiation shielding system material, thickness, and structure. The radiation shielding optimization program based on the bare-bones particle swarm optimization algorithm is developed and coupled into the reactor radiation shielding multiobjective intelligent optimization platform, and the code is verified by using the Savannah benchmark model. The material type and thickness of Savannah model were optimized by using the BBMOPSO algorithm to call the dose calculation code, the integrated optimized data showed that the weight decreased by 78.77%, the volume decreased by 23.10% and the dose rate decreased by 72.41% compared with the initial solution. The results show that the method can get the best radiation shielding solution that meets a lot of different goals. This shows that the method is both effective and feasible, and it makes up for the lack of manual optimization
Abstract
In order to further meet the requirements of weight, volume, and dose minimization for new nuclear energy devices, the bare-bones multi-objective particle swarm optimization algorithm is used to automatically and iteratively optimize the design parameters of radiation shielding system material, thickness, and structure. The radiation shielding optimization program based on the bare-bones particle swarm optimization algorithm is developed and coupled into the reactor radiation shielding multiobjective intelligent optimization platform, and the code is verified by using the Savannah benchmark model. The material type and thickness of Savannah model were optimized by using the BBMOPSO algorithm to call the dose calculation code, the integrated optimized data showed that the weight decreased by 78.77%, the volume decreased by 23.10% and the dose rate decreased by 72.41% compared with the initial solution. The results show that the method can get the best radiation shielding solution that meets a lot of different goals. This shows that the method is both effective and feasible, and it makes up for the lack of manual optimization
참고문헌 (20)
- K. P. Ananth, "Portable Special Purpose Nuclear Reactor (2 MW) for Remote Operating Bases and Microgrids", Joint Service Power Expo (2017)
- The Holos Reactor: A Distributable Power Generator with Transportable Subcritical Power Modules, "C. Filippone", ~ (2018)
- Hwanyeal Yu, "A conceptual study of a supercritical CO2-cooled micro modular reactor", Energies, 8, pp.13938~13952 (2015)
- J. C. Lei, "Prediction of burn-up nucleus density based on machine learning", International Journal of Energy Research, 45(9), pp.14052~14061 (2021)
- A. Yamaji, "Shielding design to obtain compact marine reactor", Journal of Nuclear Science and Technology, 31(6), pp.510~520 (1994)
- J. C. Lei, "Prediction of crucial nuclear power plant parameters using long short-term memory neural networks", Int. J. Energy Res, 15(46), pp.21467~21479 (2022)
- Y. Ronen, "The smallest thermal nuclear reactor", NUCLEAR SCIENCE AND ENGINEERING, 153, pp.90~92 (2006)
- L. Jichong, "Validation of Doppler temperature coefficients and component power distribution for the advanced neutronic component program KYLIN V2. 0", Frontiers in Energy Research, pp.809~ (2021)
- Hwanyeal Yu, "Neutronics and transient analyses of a supercritical CO2-cooled micro modular reactor (MMR)", Ookayama campus, Tokyo institute of technology, Japan,5th international symposium on innovative nuclear energy systems, 5 (2016), INES
- Z. Chen, "Metaheuristic optimization method for compact reactor radiation shielding design based on genetic algorithm", Annals of Nuclear Energy, 134, pp.318~329 (2019)
- X. Wu, "Multi-objective optimization method for nuclear reactor radiation shielding design based on PSO algorithm", Annals of Nuclear Energy, 160, pp.108404~ (2021)
- Z. Chen, "Multi-objective optimization strategies for radiation shielding design with genetic algorithm", Computer Physics Communications, 260, pp.107267~ (2021)
- J. Kennedy, "Particle swarm optimization", Proceedings of ICNN'95-international conference on neural networks, 4, pp.1942~1948 (1995), IEEE
- D. A. Ejigu, "Gradient descent-particle swarm optimization based deep neural network predictive control of pressurized water reactor power", Progress in Nuclear Energy, 145, pp.104108~ (2022)
- 임동원, "On the Particle Swarm Optimization of Cask Shielding Design for a prototype Sodium-cooled Fast Reactor", Nuclear Engineering and Technology, 51(1), pp.284~292 (2019), 한국원자력학회[DOI]
- W. Shu, "Application of particle swarm optimization to the reconstruction of the energy group structure for shielding library", Annals of Nuclear Energy, 175, pp.109210~ (2022)
- C. A. C. Coello, "MOPSO: a proposal for multiple objective particle swarm optimization", Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02, 2, pp.1051~1056 (2002), IEEE
- Y. Zhang, "A bare-bones multi-objective particle swarm optimization algorithm for environmental", economic dispatch, 2012, pp.192, 213~227
- The Radiation Leakage Survey of the Shield of the Nuclear Ship savannah, "E. P. Blizard", Oak Ridge National Lab, ~ (1962)
- Q. He, "NECP-MCX : a hybrid Monte-Carlo-Deterministic particle-transport code for the simulation of deep-penetration problems", Annals of Nuclear Energy, 151, pp.107978~ (2021)