Prediction of Nuclear Power Plant Operating Parameters Based on Transfer Learning between Simulation and Measurement Data

Pu Ke; Song Houde; Liu Xiaojing; Song Meiqi

doi:10.13832/j.jnpe.2024.080004

Volume 46 Issue 2

Apr. 2025

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Article Navigation > Nuclear Power Engineering > 2025 > 46(2): 261-271

Pu Ke, Song Houde, Liu Xiaojing, Song Meiqi. Prediction of Nuclear Power Plant Operating Parameters Based on Transfer Learning between Simulation and Measurement Data[J]. Nuclear Power Engineering, 2025, 46(2): 261-271. doi: 10.13832/j.jnpe.2024.080004

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Pu Ke, Song Houde, Liu Xiaojing, Song Meiqi. Prediction of Nuclear Power Plant Operating Parameters Based on Transfer Learning between Simulation and Measurement Data[J]. Nuclear Power Engineering, 2025, 46(2): 261-271. doi: 10.13832/j.jnpe.2024.080004

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Prediction of Nuclear Power Plant Operating Parameters Based on Transfer Learning between Simulation and Measurement Data

doi: 10.13832/j.jnpe.2024.080004

Pu Ke^{1, 2
,},
Song Houde^{2, 3},
Liu Xiaojing^{2, 3
,
,},
Song Meiqi^{1, 2}

1.
College of Smart Energy, Shanghai Jiao Tong University, Shanghai, 200240, China
2.
Shanghai Digital Nuclear Reactor Technology Integration Innovation Center, Shanghai, 200240, China
3.
School of Nuclear Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

Received Date: 2024-07-30
Accepted Date: 2025-01-15
Rev Recd Date: 2024-09-08

Available Online: 2025-01-15

Publish Date: 2025-04-15

Abstract

Abstract

The key to the safe operation of nuclear power plants is to achieve accurate prediction of their operating parameters. In recent years, data-driven methods have shown strong predictive capabilities. However, insufficient measurement data limits their predictive performance. Based on the transfer learning framework, this study develops a prediction model construction method that is pre-trained with multiple sets of simulation conditions and then fine-tuned with measured data. First, the Gated Recurrent Unit (GRU) neural network is trained with simulation data, and then the model is fine-tuned using part of the measurement data to predict the future state of the operating conditions. The feasibility of the method is verified using the measurement data of the B3.1 experiment on the PKL Ⅲ thermal hydraulic bench and 9 sets of similar RELAP5 simulation data. Using this method, the relative errors of steam pressure, steam temperature, downcomer fluid temperature, outlet temperature, inlet temperature and mass flow rate can reach 0.358%, 0.065%, 0.020%, 0.065%, 0.028% and 1.705%, respectively. Finally, five sets of numerical experiments are used to compare and illustrate the effectiveness of each module of the method.
- Transfer learning,
- Operating parameter prediction,
- Gated Recurrent Unit (GRU) neural network

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References(14)

References

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