Prediction of Vertical-Downward Two-Phase Flow Pattern based on PCA-GA-SVM

Qiao Shouxu; Zhong Wenyi; Tan Sichao; Li Xupeng; Hao Sijia

doi:10.13832/j.jnpe.2022.03.0085

Volume 43 Issue 3

Jun. 2022

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Article Navigation > Nuclear Power Engineering > 2022 > 43(3): 85-93

Zhang Ying. Optimization Design for Reactor Control System of the First Overseas HPR1000 Reactor[J]. Nuclear Power Engineering, 2022, 43(5): 245-249. doi: 10.13832/j.jnpe.2022.05.0245

Citation:

Qiao Shouxu, Zhong Wenyi, Tan Sichao, Li Xupeng, Hao Sijia. Prediction of Vertical-Downward Two-Phase Flow Pattern based on PCA-GA-SVM[J]. Nuclear Power Engineering, 2022, 43(3): 85-93. doi: 10.13832/j.jnpe.2022.03.0085

Zhang Ying. Optimization Design for Reactor Control System of the First Overseas HPR1000 Reactor[J]. Nuclear Power Engineering, 2022, 43(5): 245-249. doi: 10.13832/j.jnpe.2022.05.0245

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Prediction of Vertical-Downward Two-Phase Flow Pattern based on PCA-GA-SVM

doi: 10.13832/j.jnpe.2022.03.0085

1.
Heilongjiang Provincial Key Laboratory of Nuclear Power System & Equipment, Harbin Engineering University, Harbin, 150001, China
2.
China Institute of Atomic Energy, Beijing, 102413, China

Received Date: 2021-04-06
Accepted Date: 2021-12-09
Rev Recd Date: 2021-11-26
Publish Date: 2022-06-07

Abstract

Abstract

In order to improve the accuracy and timeliness of flow pattern identification under the condition of small samples, an optimized identification model integrating wavelet packet decomposition (WPD), principal component analysis (PCA), genetic algorithm (GA) and support vector machine (SVM) is proposed and successfully applied to the flow pattern recognition of vertical-downward two-phase flow. WPD is used to decompose and reconstruct the non-stationary conductivity fluctuation signal, extract the wavelet packet energy and construct the feature vector; The dimension of feature vector is reduced by PCA to reduce the complexity of feature input; At the same time, the key parameters penalty factor (C) and kernel function parameter (g) of SVM are determined by GA global iterative optimization. After verifying the identification effect of PCA-GA-SVM, it is compared with SVM, PCA-SVM and GA-SVM networks. The results show that the SVM network optimized by PCA and GA is significantly improved in terms of flow pattern identification accuracy and timeliness. The overall prediction accuracy of bubble flow, slug flow, stirred flow and annular flow reaches 94.87%, and the time consumed is only 3.95 s, which can meet the needs of on-line identification.
- Vertical-downward tube,
- Two-phase flow,
- Support vector machine (SVM),
- Principal component analysis (PCA),
- Genetic algorithm (GA),
- Flow pattern prediction

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References(19)

References

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