Single-channel Temperature Prediction of Heat Pipe Reactor Based on Deep Neural Network

Yu Xin; Wang Jiajun; Guo Kailun; Zhang Zeqin; Tian Wenxi; Su Guanghui; Qiu Suizheng

doi:10.13832/j.jnpe.2025.S1.0075

Volume 46 Issue S1

Jul. 2025

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Article Navigation > Nuclear Power Engineering > 2025 > 46(S1): 75-81

Yu Xin, Wang Jiajun, Guo Kailun, Zhang Zeqin, Tian Wenxi, Su Guanghui, Qiu Suizheng. Single-channel Temperature Prediction of Heat Pipe Reactor Based on Deep Neural Network[J]. Nuclear Power Engineering, 2025, 46(S1): 75-81. doi: 10.13832/j.jnpe.2025.S1.0075

Citation:

Yu Xin, Wang Jiajun, Guo Kailun, Zhang Zeqin, Tian Wenxi, Su Guanghui, Qiu Suizheng. Single-channel Temperature Prediction of Heat Pipe Reactor Based on Deep Neural Network[J]. Nuclear Power Engineering, 2025, 46(S1): 75-81. doi: 10.13832/j.jnpe.2025.S1.0075

Citation:

Yu Xin, Wang Jiajun, Guo Kailun, Zhang Zeqin, Tian Wenxi, Su Guanghui, Qiu Suizheng. Single-channel Temperature Prediction of Heat Pipe Reactor Based on Deep Neural Network[J]. Nuclear Power Engineering, 2025, 46(S1): 75-81. doi: 10.13832/j.jnpe.2025.S1.0075

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Single-channel Temperature Prediction of Heat Pipe Reactor Based on Deep Neural Network

doi: 10.13832/j.jnpe.2025.S1.0075

School of Nuclear Science and Technology, Xi’an Jiaotong University, Xi’an, 710049, China

Received Date: 2024-09-13
Rev Recd Date: 2025-01-20
Publish Date: 2025-07-09

Abstract

Abstract

Heat pipe reactors have become a strong candidate for nuclear power generation due to their unique design and efficient heat conduction performance. However, accurate monitoring of the core temperature field remains a key challenge. This paper explores a novel method for rapid prediction of the core temperature field based on deep learning technology. By establishing a backpropagation neural network (BPNN) model and training a large amount of core numerical simulation data, it is possible to predict the temperature field of a single channel core section using 6 temperature measurement points. The training results of the model show that selecting the appropriate number of neurons and hidden layers can effectively improve prediction accuracy and reduce the risk of overfitting. The neural network model in this study has an average absolute error of 1.06 K on the test set, demonstrating good predictive ability and a low level of error. Errors are primarily concentrated in corner fuel rods and regions with intense heat exchange.
- Heat pipe reactor,
- Solid-state reactor core,
- Deep learning,
- Backpropagation neural network (BPNN),
- Temperature prediction

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References(11)

References

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