Research on Rapid Prediction of 3D Steady-State Temperature Field of Steam Generators Based on Deep Learning

Ye Yibo; He Shaopeng; Wang Mingjun; Tian Wenxi; Qiu Suizheng; Su Guanghui

doi:10.13832/j.jnpe.2024.090064

Volume 46 Issue 5

Oct. 2025

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Article Navigation > Nuclear Power Engineering > 2025 > 46(5): 274-284

Ye Yibo, He Shaopeng, Wang Mingjun, Tian Wenxi, Qiu Suizheng, Su Guanghui. Research on Rapid Prediction of 3D Steady-State Temperature Field of Steam Generators Based on Deep Learning[J]. Nuclear Power Engineering, 2025, 46(5): 274-284. doi: 10.13832/j.jnpe.2024.090064

Citation:

Ye Yibo, He Shaopeng, Wang Mingjun, Tian Wenxi, Qiu Suizheng, Su Guanghui. Research on Rapid Prediction of 3D Steady-State Temperature Field of Steam Generators Based on Deep Learning[J]. Nuclear Power Engineering, 2025, 46(5): 274-284. doi: 10.13832/j.jnpe.2024.090064

Citation:

Ye Yibo, He Shaopeng, Wang Mingjun, Tian Wenxi, Qiu Suizheng, Su Guanghui. Research on Rapid Prediction of 3D Steady-State Temperature Field of Steam Generators Based on Deep Learning[J]. Nuclear Power Engineering, 2025, 46(5): 274-284. doi: 10.13832/j.jnpe.2024.090064

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Research on Rapid Prediction of 3D Steady-State Temperature Field of Steam Generators Based on Deep Learning

doi: 10.13832/j.jnpe.2024.090064

Ye Yibo^{1, 2
,},
He Shaopeng^{1, 2},
Wang Mingjun^{1, 2
,
,},
Tian Wenxi^{1, 2},
Qiu Suizheng^{1, 2},
Su Guanghui^{1, 2}

1.
State Key Laboratory of Multiphase Flow in Power Engineering, Xi’an Jiaotong University, Xi’an, 710049, China
2.
Nuclear Thermal-hydraulic Laboratory, Xi’an Jiaotong University, Xi’an, 710049, China

Received Date: 2024-09-20
Rev Recd Date: 2025-03-31

Available Online: 2025-10-15

Publish Date: 2025-10-15

Abstract

Abstract

To establish a fast prediction method for the three-dimensional steady-state temperature field of a steam generator and enable fast computation of the steady-state response of key nuclear reactor equipment under different operating conditions, this study employs Convolutional Neural Network (CNN) and Transformer algorithms to predict the 3D steady-state temperature field of vertical natural circulation U-tube steam generators. Steady-state temperature distributions across multiple planes of the steam generator under different boundary conditions are obtained through Computational Fluid Dynamics (CFD) simulations. These results are then processed with interpolation methods to create training and testing datasets suitable for deep learning algorithms. The aforementioned deep learning algorithms are trained using the training dataset, and their performance is evaluated using the testing dataset. The results show that when predicting the 3D steady-state temperature field of steam generators, deep learning algorithms can reduce prediction time to 0.3 seconds, with temperature prediction errors not exceeding 1 K, enabling real-time prediction of the 3D steady-state temperature field.
- Convolutional neural network algorithm,
- Transformer algorithm,
- Steam generator,
- Rapid prediction,
- Three-dimensional temperature field

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References(16)

References

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