Date:

2025-10-16

Title of Paper:

Prediction of steam generator liquid level under main steam line break accident based on wavelet decomposition combined with deep learning

Journal:

Nuclear Engineering and Design

Summary:

Liquid level monitoring is essential for maintaining the safe operation of nuclear power circuits. During a Main
Steam Line Break (MSLB) accident, signiffcant ffuctuations in the liquid level within the steam generator pose
challenges for traditional measurement methods, which often fail to accurately capture the true liquid level. This
study conducted experiments of MSLB accidents under controlled conditions, with parameters including heating
power ranging from 8 to 16 kW, break pressures from 0.05 to 0.1 MPa, and relative break sizes between 20 % and
100 %. In selected conditions, rolling motions were introduced to simulate marine environments. Wavelet
decomposition was utilized to extract features at varying frequency levels, and deep learning models were
employed to predict each component. The proposed approach achieved a prediction accuracy of 88.3 %, outperforming
direct predictions from raw data with improvements of 21.9 % in Mean Squared Error (MSE), 12.3 %
in Mean Absolute Error (MAE), and 10.0 % in the coefffcient of determination (R2
). The detail component cD1
was found to have the most signiffcant impact on overall prediction accuracy, highlighting it as a key parameter
for further optimization. Furthermore, the use of wavelet-decomposed data signiffcantly reduced computational
complexity, enhancing time efffciency. These results demonstrate the effectiveness of the proposed method in
improving prediction accuracy and operational efffciency, offering valuable support for the safe management of
nuclear power systems during MSLB accidents.

Co-author:

Biaoxin Wang,Yuang Jiang, Mei Lin, Qiuwang Wang

Volume:

436

Page Number:

113998

Translation or Not:

No

Date of Publication:

2025-03-02