Research on Rapid Reconstruction Technology of Temperature Field in Heat Transfer Tube of Steam Generator Based on POD and Neural Network

Zhang He; Liang Biao; Wang Bo; Tan Sichao; Han Rui; Li Jiangkuan; Tian Ruifeng

doi:10.13832/j.jnpe.2024.070047

Volume 46 Issue 2

Apr. 2025

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Article Navigation > Nuclear Power Engineering > 2025 > 46(2): 90-97

Zhang He, Liang Biao, Wang Bo, Tan Sichao, Han Rui, Li Jiangkuan, Tian Ruifeng. Research on Rapid Reconstruction Technology of Temperature Field in Heat Transfer Tube of Steam Generator Based on POD and Neural Network[J]. Nuclear Power Engineering, 2025, 46(2): 90-97. doi: 10.13832/j.jnpe.2024.070047

Citation:

Zhang He, Liang Biao, Wang Bo, Tan Sichao, Han Rui, Li Jiangkuan, Tian Ruifeng. Research on Rapid Reconstruction Technology of Temperature Field in Heat Transfer Tube of Steam Generator Based on POD and Neural Network[J]. Nuclear Power Engineering, 2025, 46(2): 90-97. doi: 10.13832/j.jnpe.2024.070047

Citation:

Zhang He, Liang Biao, Wang Bo, Tan Sichao, Han Rui, Li Jiangkuan, Tian Ruifeng. Research on Rapid Reconstruction Technology of Temperature Field in Heat Transfer Tube of Steam Generator Based on POD and Neural Network[J]. Nuclear Power Engineering, 2025, 46(2): 90-97. doi: 10.13832/j.jnpe.2024.070047

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Research on Rapid Reconstruction Technology of Temperature Field in Heat Transfer Tube of Steam Generator Based on POD and Neural Network

doi: 10.13832/j.jnpe.2024.070047

Zhang He^{1, 2
,},
Liang Biao^{1, 2},
Wang Bo^{1, 2
,
,},
Tan Sichao^{1, 2},
Han Rui^{1, 2},
Li Jiangkuan^{1, 2},
Tian Ruifeng^{1, 2}

1.
Heilongjiang Provincial Key Laboratory of Nuclear Power System & Equipment, Harbin Engineering University, Harbin, 150001, China
2.
College of Nuclear Science and Technology, Harbin Engineering University, Harbin, 150001, China

Received Date: 2024-07-17
Rev Recd Date: 2024-07-29

Available Online: 2025-01-23

Publish Date: 2025-04-02

Abstract

Abstract

The secondary flow region of a casing once-through steam generator involves complex two-phase flow. Although numerical simulation methods can achieve precise simulation calculations, they are slow and time-consuming for multi-condition and transient calculations, and consume significant computational resources. Model reduction is a method that transforms a complex system into an approximately simplified system, enabling rapid calculations while retaining the main characteristics of the original system. This study employs the Proper Orthogonal Decomposition (POD) method to reduce the model of the temperature field inside the heat exchange tubes, capturing the modal coefficients by projecting the original complex system onto a limited number of modes. A neural network method is applied to capture the distribution patterns of short- and long-term time series modal coefficients. The research results indicate that the error in predicting the reconstructed temperature field is within 15%, and the prediction speed is improved by four orders of magnitude compared to numerical simulation methods. Therefore, the prediction method established in this study, which couples model order reduction with neural networks, can be utilized for the rapid prediction of the temperature field within the casing, providing support for internal thermal-hydraulic analysis.
- Temperature field,
- Model reduction,
- Neural network,
- Rapid prediction

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References(14)

References

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