Identification of Flow Regime of Boiling Flow in a Vertical Annulus with Unsupervised Machine Learning

Zhu Longxiang; Zhang Luteng; Sun Wan; Ma Zaiyong; Pan Liangming

doi:10.13832/j.jnpe.2023.03.0112

Volume 44 Issue 3

Jun. 2023

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Zhu Longxiang, Zhang Luteng, Sun Wan, Ma Zaiyong, Pan Liangming. Identification of Flow Regime of Boiling Flow in a Vertical Annulus with Unsupervised Machine Learning[J]. Nuclear Power Engineering, 2023, 44(3): 112-120. doi: 10.13832/j.jnpe.2023.03.0112

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Zhu Longxiang, Zhang Luteng, Sun Wan, Ma Zaiyong, Pan Liangming. Identification of Flow Regime of Boiling Flow in a Vertical Annulus with Unsupervised Machine Learning[J]. Nuclear Power Engineering, 2023, 44(3): 112-120. doi: 10.13832/j.jnpe.2023.03.0112

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Identification of Flow Regime of Boiling Flow in a Vertical Annulus with Unsupervised Machine Learning

doi: 10.13832/j.jnpe.2023.03.0112

Zhu Longxiang^{1, 2, 3
,},
Zhang Luteng^{1, 3},
Sun Wan^{1, 3},
Ma Zaiyong^{1, 3},
Pan Liangming^{1, 3}

1.
Key Laboratory of Low-grade Energy Utilization Technologies & Systems, MOE, Chongqing University, Chongqing, 400044, China
2.
Postdoctoral Station of Power Engineering and Engineering Thermophysics, Chongqing University, Chongqing, 400044, China
3.
Department of Nuclear Engineering and Technology, Chongqing University, Chongqing, 400044, China

Received Date: 2022-06-06
Rev Recd Date: 2022-09-15
Publish Date: 2023-06-15

Abstract

Abstract

Accurate flow regime identification of the boiling flow is of great significance for using closure correlations in thermal-hydraulic system codes. The flow regime identification is achieved by the unsupervised machine learning (ML) approach, and the current work integrates the data-driven ML model with the two-phase-flow domain knowledge. Two requirements are established to determine whether the feed-in data type is appropriate or not: ①the information captured by the unsupervised ML should be regime-relevant features for distinguishing flow regimes;②the cluster criterion for a flow regime should cover all the possible feed-in features of the regime. Feed-in data types generated by the conductivity probe are examined; among them, only the bubble chord length Cumulative Distribution Function (CDF) fulfills the two requirements. With the feed-in data of chord length CDF, the two-dimensional local flow regimes are identified and analyzed for a boiling dataset conducted in an internally heated vertical annulus. The results show that the higher-rank regimes appear at the channel’s center leaning toward the inner-heated wall. The global flow regime map is obtained with the local regimes, and a new flow regime transition criterion between the bubbly and slug flow is developed as the void fraction equals 0.14.
- Two-phase flow,
- Flow regime,
- Bubble size distribution,
- Machine learning,
- Feed-in feature

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References(19)

References

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