Research on PWR Core Refueling Optimization Method Based on Bayesian Optimization

Zhou Yuancheng; Li Yunzhao; Wu Hongchun

doi:10.13832/j.jnpe.2024.09.0003

Volume 46 Issue 2

Apr. 2025

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Article Navigation > Nuclear Power Engineering > 2025 > 46(2): 202-208

Zhou Yuancheng, Li Yunzhao, Wu Hongchun. Research on PWR Core Refueling Optimization Method Based on Bayesian Optimization[J]. Nuclear Power Engineering, 2025, 46(2): 202-208. doi: 10.13832/j.jnpe.2024.09.0003

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Zhou Yuancheng, Li Yunzhao, Wu Hongchun. Research on PWR Core Refueling Optimization Method Based on Bayesian Optimization[J]. Nuclear Power Engineering, 2025, 46(2): 202-208. doi: 10.13832/j.jnpe.2024.09.0003

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Research on PWR Core Refueling Optimization Method Based on Bayesian Optimization

doi: 10.13832/j.jnpe.2024.09.0003

School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an, 710000, China

Received Date: 2024-09-20
Accepted Date: 2024-11-13
Rev Recd Date: 2024-11-13

Available Online: 2025-04-02

Publish Date: 2025-04-02

Abstract

Abstract

Refueling optimization for pressurized water reactor (PWR) cores is crucial for the safe, efficient, and cost-effective operation of nuclear power plants, which is a constrained, nonlinear, non-convex integer combinatorial optimization challenge. Traditional methods often struggle with low computational efficiency and the risk of getting trapped in local optima. This paper presents a refueling optimization approach based on variational autoencoders, deep metric learning, and Bayesian optimization. The method leverages variational autoencoders to map discrete core layout configurations into a continuous latent space. Deep metric learning is then used to construct the latent space such that samples with similar core physical characteristics are positioned closer together. A multi-objective Bayesian optimization is subsequently applied to efficiently search for optimal solutions in this latent space, and a decoder transforms the optimal latent variables back into corresponding core layouts. Experimental validation using the first-cycle initial loading data of an M310 core demonstrates that this method significantly improves refueling optimization efficiency and solution quality, producing better configurations than traditional methods.
- Refueling optimization,
- Bayesian optimization,
- Variational autoencoder,
- Deep metric learning,
- NECP-Bamboo

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References

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