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

Turn off MathJax

Article Contents

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

Citation:

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

Citation:

PDF( 9841 KB)

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

FullText(HTML)

References(29)

References

[1]	LI Z, WANG J C, DING M. A review on optimization methods for nuclear reactor fuel reloading analysis[J]. Nuclear Engineering and Design, 2022, 397: 111950. doi: 10.1016/j.nucengdes.2022.111950
[2]	KIRKPATRICK S, GELATT JR C D, VECCHI M P. Optimization by simulated annealing[J]. Science, 1983, 220(4598): 671-680. doi: 10.1126/science.220.4598.671
[3]	HOLLAND J H. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence[M]. Cambridge: MIT Press, 1992.
[4]	KENNEDY J, EBERHART R. Particle swarm optimization[C]//Proceedings of ICNN’95-International Conference on Neural Networks. Perth: IEEE, 1995: 1942-1948.
[5]	ERDOĞAN A, GEÇKINLI M. A PWR reload optimisation code (XCore) using artificial neural networks and genetic algorithms[J]. Annals of Nuclear Energy, 2003, 30(1): 35-53. doi: 10.1016/S0306-4549(02)00041-5
[6]	WAN C H, LEI K H, LI Y S. Optimization method of fuel-reloading pattern for PWR based on the improved convolutional neural network and genetic algorithm[J]. Annals of Nuclear Energy, 2022, 171: 109028. doi: 10.1016/j.anucene.2022.109028
[7]	LI Z, WANG J C, HUANG J, et al. Development and research of triangle-filter convolution neural network for fuel reloading optimization of block-type HTGRs[J]. Applied Soft Computing, 2022, 136: 110126.
[8]	JIN Y C. Surrogate-assisted evolutionary computation: recent advances and future challenges[J]. Swarm and Evolutionary Computation, 2011, 1(2): 61-70. doi: 10.1016/j.swevo.2011.05.001
[9]	JONES D R, SCHONLAU M, WELCH W J. Efficient global optimization of expensive black-box functions[J]. Journal of Global Optimization, 1998, 13(4): 455-492. doi: 10.1023/A:1008306431147
[10]	WANG X L, JIN Y C, SCHMITT S, et al. Recent advances in Bayesian optimization[J]. ACM Computing Surveys, 2023, 55(13s): 287. doi: 10.1145/3582078
[11]	SHAHRIARI B, SWERSKY K, WANG Z Y, et al. Taking the human out of the loop: a review of Bayesian optimization[J]. Proceedings of the IEEE, 2016, 104(1): 148-175. doi: 10.1109/JPROC.2015.2494218
[12]	SEEGER M. Gaussian processes for machine learning[J]. International Journal of Neural Systems, 2004, 14(2): 69-106. doi: 10.1142/S0129065704001899
[13]	MOČKUS J. On Bayesian methods for seeking the extremum[C]//Proceedings of the IFIP Technical Conference on Optimization Techniques. Novosibirsk: Springer, 1974: 400-404.
[14]	AUER P. Finite-time Analysis of the Multiarmed Bandit Problem[Z]. Kluwer Academic Publishers, 2002.
[15]	KINGMA D P, WELLING M. Auto-encoding variational Bayes[EB/OL]. arXiv, 2022. [2024-07-01]. http://arxiv.org/abs/1312.6114.
[16]	KAYA M, BİLGE H Ş. Deep metric learning: a survey[J]. Symmetry, 2019, 11(9): 1066. doi: 10.3390/sym11091066
[17]	ISHFAQ H, HOOGI A, RUBIN D. TVAE: triplet-based variational autoencoder using metric learning[C]//Proceedings of the 6th International Conference on Learning Representations. Vancouver: ICLR, 2018.
[18]	KOGE D, ONO N, HUANG M, et al. Embedding of molecular structure using molecular hypergraph variational autoencoder with metric learning[J]. Molecular Informatics, 2021, 40(2): e2000203. doi: 10.1002/minf.202000203
[19]	DAULTON S, ERIKSSON D, BALANDAT M, et al. Multi-objective Bayesian optimization over high-dimensional search spaces[EB/OL]. arXiv, 2022. [2024-04-22]. http://arxiv.org/abs/2109.10964.
[20]	VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach: Curran Associates Inc. , 2017: 6000-6010.
[21]	YU W H, SI C Y, ZHOU P, et al. MetaFormer baselines for vision[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, 46(2): 896-912. doi: 10.1109/TPAMI.2023.3329173
[22]	GROSNIT A, TUTUNOV R, MARAVAL A M, et al. High-dimensional Bayesian optimisation with variational autoencoders and deep metric learning[EB/OL]. arXiv, 2021. [2024-02-26]. http://arxiv.org/abs/2106.03609.
[23]	MCKAY M D, BECKMAN R J, CONOVER W J. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code[J]. Technometrics, 2000, 42(1): 55-61. doi: 10.1080/00401706.2000.10485979
[24]	SRINIVAS N, DEB K. Muiltiobjective optimization using nondominated sorting in genetic algorithms[J]. Evolutionary Computation, 1994, 2(3): 221-248. doi: 10.1162/evco.1994.2.3.221
[25]	TRIPP A, DAXBERGER E, HERNÁNDEZ-LOBATO J M. Sample-efficient optimization in the latent space of deep generative models via weighted retraining[C]//Proceedings of the 34th International Conference on Neural Information Processing Systems. Vancouver: Curran Associates Inc. , 2020: 945.
[26]	梁毅琳,李云召,周原成,等.基于核电厂实测数据的NECP-Bamboo软件验证与确认[J].核动力工程, 2024, 45(2):24-34. 梁毅琳, 李云召, 周原成, 等.基于核电厂实测数据的NECP-Bamboo软件验证与确认[J].核动力工程, 2024, 45(2):24-34.
[27]	李倩倩. 基于压水堆换料优化基准问题的随机优化方法的机理及应用研究[D]. 上海: 上海交通大学,2010.
[28]	DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197. doi: 10.1109/4235.996017
[29]	BLANK J, DEB K. Pymoo: multi-objective optimization in python[J]. IEEE Access, 2020, 8: 89497-89509. doi: 10.1109/ACCESS.2020.2990567