Research on Algorithm of Solving Neutron Equation Based on ResNet-PINN

Niu Yixiao; Li Jiafang; Yang Chun; Liu Yang; Lai Qiuyu; Fu Meirui; Jiang Yi

doi:10.13832/j.jnpe.2024.080035

Volume 46 Issue 2

Apr. 2025

Turn off MathJax

Article Contents

Article Navigation > Nuclear Power Engineering > 2025 > 46(2): 76-80

Niu Yixiao, Li Jiafang, Yang Chun, Liu Yang, Lai Qiuyu, Fu Meirui, Jiang Yi. Research on Algorithm of Solving Neutron Equation Based on ResNet-PINN[J]. Nuclear Power Engineering, 2025, 46(2): 76-80. doi: 10.13832/j.jnpe.2024.080035

Citation:

Niu Yixiao, Li Jiafang, Yang Chun, Liu Yang, Lai Qiuyu, Fu Meirui, Jiang Yi. Research on Algorithm of Solving Neutron Equation Based on ResNet-PINN[J]. Nuclear Power Engineering, 2025, 46(2): 76-80. doi: 10.13832/j.jnpe.2024.080035

Citation:

PDF( 5644 KB)

Research on Algorithm of Solving Neutron Equation Based on ResNet-PINN

doi: 10.13832/j.jnpe.2024.080035

1.
Key Laboratory of Land Resources Evaluation and Monitoring in Southwest China, Ministry of Education, Sichuan Normal University, Chengdu, 610068, China
2.
Chengdu State-owned Jinjiang Machine Factory, Chengdu, 610043, China
3.
School of Computer Science, Sichuan University, Chengdu, 610065, China

Received Date: 2024-08-14
Rev Recd Date: 2024-09-14

Available Online: 2025-01-15

Publish Date: 2025-04-02

Abstract

Abstract

As a deep learning method integrating physical knowledge, the Physics-Informed Neural Network (PINN) has certain limitations in terms of the accuracy of problem-solving. To further enhance the solution accuracy of the PINN model, an improved PINN model based on the Residual Network (ResNet) structure (ResNet-PINN) is proposed. The basic principle and numerical calculation process of ResNet-PINN are elaborated in detail, and it is applied to the solution of neutron diffusion and transport equations in the nuclear field. Experimental validation has shown that ResNet-PINN improves the solution accuracy of the reactor core neutron diffusion equation by a factor of 2 to 10 times, and enhances the solution accuracy of the transport equation by a factor of 3 to 6 times., effectively solving the solution accuracy limitations faced by the PINN model.
- Physics-Informed Neural Network (PINN),
- Residual Network (ResNet),
- Neutron diffusion equation,
- Neutron transport equation

FullText(HTML)

References(12)

References

[1]	RAISSI M, PERDIKARIS P, KARNIADAKIS G E. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations[J]. Journal of Computational Physics, 2019, 378: 686-707. doi: 10.1016/j.jcp.2018.10.045
[2]	XIE Y C, WANG Y H, MA Y, et al. Neural network based deep learning method for multi-dimensional neutron diffusion problems with novel treatment to boundary[J]. Journal of Nuclear Engineering, 2021, 2(4): 533-552. doi: 10.3390/jne2040036
[3]	刘东,罗琦,唐雷,等. 基于PINN深度机器学习技术求解多维中子学扩散方程[J]. 核动力工程,2022, 43(2): 1-8.
[4]	WANG J Y, PENG X J, CHEN Z, et al. Surrogate modeling for neutron diffusion problems based on conservative physics-informed neural networks with boundary conditions enforcement[J]. Annals of Nuclear Energy, 2022, 176: 109234. doi: 10.1016/j.anucene.2022.109234
[5]	ELHAREEF M H, WU Z Y. Physics-informed neural network method and application to nuclear reactor calculations: a pilot study[J]. Nuclear Science and Engineering, 2023, 197(4): 601-622. doi: 10.1080/00295639.2022.2123211
[6]	刘东,王雪强,张斌,等. 深度学习方法求解中子输运方程的微分变阶理论[J]. 原子能科学技术,2023, 57(5): 946-959. doi: 10.7538/yzk.2023.youxian.0002
[7]	XIE Y C, WANG Y H, MA Y. Boundary dependent physics-informed neural network for solving neutron transport equation[J]. Annals of Nuclear Energy, 2024, 195: 110181. doi: 10.1016/j.anucene.2023.110181
[8]	HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//29th IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 770-778.
[9]	郭玥秀,杨伟,刘琦,等. 残差网络研究综述[J]. 计算机应用研究,2020, 37(5): 1292-1297.
[10]	曹良志,谢仲生,李云召. 近代核反应堆物理分析[M]. 北京: 中国原子能出版社,2017:12-20 .
[11]	谢仲生,曹良志,张少泓. 核反应堆物理分析[M]. 第五版. 西安: 西安交通大学出版社,2020: 69-90.
[12]	杜书华. 输运问题的计算机模拟[M]. 长沙: 湖南科学技术出版社,1989: 467-470.