Comparative Study of Neural Network-based Source Term Inversion Methods for Nuclear Accidents

Peng Dingping; Li Zhonghao; Cao Bo; Miao Xuewei; You Qingyue

doi:10.13832/j.jnpe.2024.S2.0214

Volume 45 Issue S2

Jan. 2025

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Article Navigation > Nuclear Power Engineering > 2024 > 45(S2): 214-222

Peng Dingping, Li Zhonghao, Cao Bo, Miao Xuewei, You Qingyue. Comparative Study of Neural Network-based Source Term Inversion Methods for Nuclear Accidents[J]. Nuclear Power Engineering, 2024, 45(S2): 214-222. doi: 10.13832/j.jnpe.2024.S2.0214

Citation:

Peng Dingping, Li Zhonghao, Cao Bo, Miao Xuewei, You Qingyue. Comparative Study of Neural Network-based Source Term Inversion Methods for Nuclear Accidents[J]. Nuclear Power Engineering, 2024, 45(S2): 214-222. doi: 10.13832/j.jnpe.2024.S2.0214

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Comparative Study of Neural Network-based Source Term Inversion Methods for Nuclear Accidents

doi: 10.13832/j.jnpe.2024.S2.0214

School of Nuclear Science and Engineering, North China Electric Power University, Beijing, 102206, China

Received Date: 2024-06-14
Rev Recd Date: 2024-09-30
Publish Date: 2025-01-06

Abstract

Abstract

The nuclear accident source term is an important basis for the evaluation of the consequences of nuclear accidents. The accurate and fast acquisition of the source term is of great significance for the development of nuclear accident emergency response decision-making. This study utilizes three typical neural network algorithms, namely radial basis neural network, back propagation neural network and deep feed-forward neural network, to compare the inverse estimation of nuclear accident source terms. For the inversion of single nuclide and multi-nuclide source term, taking the release rate of radionuclides as the inversion target, the simulation experiment is conducted to obtain the monitoring data by using the radioactive atmospheric dispersion simulation code RADC developed by the group. The results of error analysis and inversion time comparison show that the nuclide release rate inversion model based on deep feedforward neural network exhibits excellent performance. For the release rate inversion of single nuclide, the relative error of the inversion is within 1.7%~3.0% and the average absolute percentage error is 2.2% under the ten-fold cross-validation. For the multi-nuclide case, the average absolute percentage error of the inversion of deep feedforward neural network is 8.05%. Its inversion estimation accuracy and stability are better than that of the BP neural network and radial basis neural network.
- Nuclear accidents,
- Source term inversion,
- Neural network,
- Radionuclide release rate

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References(22)

References

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