Intelligent Optimization of Lead-bismuth Reactor Core Based on Radial Basis Function Surrogate Model and Niche Genetic Algorithm

Li Qiong; Liu Zijing; Wang Weijia; Zhao Pengcheng; Yu Tao; Chang Haotong

doi:10.13832/j.jnpe.2022.06.0093

Volume 43 Issue 6

Dec. 2022

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Article Navigation > Nuclear Power Engineering > 2022 > 43(6): 93-100

Li Qiong, Liu Zijing, Wang Weijia, Zhao Pengcheng, Yu Tao, Chang Haotong. Intelligent Optimization of Lead-bismuth Reactor Core Based on Radial Basis Function Surrogate Model and Niche Genetic Algorithm[J]. Nuclear Power Engineering, 2022, 43(6): 93-100. doi: 10.13832/j.jnpe.2022.06.0093

Citation:

Li Qiong, Liu Zijing, Wang Weijia, Zhao Pengcheng, Yu Tao, Chang Haotong. Intelligent Optimization of Lead-bismuth Reactor Core Based on Radial Basis Function Surrogate Model and Niche Genetic Algorithm[J]. Nuclear Power Engineering, 2022, 43(6): 93-100. doi: 10.13832/j.jnpe.2022.06.0093

Citation:

Li Qiong, Liu Zijing, Wang Weijia, Zhao Pengcheng, Yu Tao, Chang Haotong. Intelligent Optimization of Lead-bismuth Reactor Core Based on Radial Basis Function Surrogate Model and Niche Genetic Algorithm[J]. Nuclear Power Engineering, 2022, 43(6): 93-100. doi: 10.13832/j.jnpe.2022.06.0093

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Intelligent Optimization of Lead-bismuth Reactor Core Based on Radial Basis Function Surrogate Model and Niche Genetic Algorithm

doi: 10.13832/j.jnpe.2022.06.0093

Li Qiong^{1, 2
,},
Liu Zijing^{1, 2
,
,},
Wang Weijia¹,
Zhao Pengcheng^{1, 2},
Yu Tao^{1, 2},
Chang Haotong¹

1.
School of Nuclear Science and Technology, University of South China, Hengyang, Hunan, 421001, China
2.
Hunan Engineering & Technology Research Center for Virtual Nuclear Reactor, University of South China, Hengyang, Hunan, 421001, China

Received Date: 2021-11-22
Rev Recd Date: 2022-01-06
Publish Date: 2022-12-14

Abstract

Abstract

In order to solve the complex nonlinear multi-dimensional optimization problem under the influence of multi-factor coupling of lead-bismuth reactor, an intelligent optimization method for reactor core was constructed based on radial basis function (RBF) surrogate model prediction, orthogonal Latin hypercube sampling (OLHS) and niche genetic algorithm optimization. A design optimization platform for lead-bismuth reactor was developed, which included the functions of sampling, Monte Carlo program coupling treatment, and core parameter prediction and optimization. The scheme optimization verification was carried out with the minimum fuel loading of the core as the optimization objective. The results show that the RBF surrogate model can accurately and quickly predict the core characteristic parameters of the lead-bismuth reactor. Compared with the calculated values of the Monte Carlo program, the relative error of the predicted core effective multiplication factor k eff is within ± 0.1%. This intelligent optimization method is feasible for lead-bismuth reactor core optimization, which can find the optimal target scheme under the constraint of multi-factor co-variation, and greatly reduce the search calculation time of the design scheme. Therefore, the intelligent optimization method established in this study can provide new ideas for the optimization design of multi-physics, multi-variable and multi-constraint coupling effects of lead-bismuth reactor.

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References(29)

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