Development of Prediction Model for Two-phase Flow Regime in Nuclear Reactor Core Based on Artificial Neural Network

Ma Yichao; Kong Dexiang; Tian Wenxi; Zhang Jing; Wu Yingwei; Qiu Suizheng; Su Guanghui

doi:10.13832/j.jnpe.2024.090038

Volume 46 Issue 2

Apr. 2025

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Article Navigation > Nuclear Power Engineering > 2025 > 46(2): 156-163

Ma Yichao, Kong Dexiang, Tian Wenxi, Zhang Jing, Wu Yingwei, Qiu Suizheng, Su Guanghui. Development of Prediction Model for Two-phase Flow Regime in Nuclear Reactor Core Based on Artificial Neural Network[J]. Nuclear Power Engineering, 2025, 46(2): 156-163. doi: 10.13832/j.jnpe.2024.090038

Citation:

Ma Yichao, Kong Dexiang, Tian Wenxi, Zhang Jing, Wu Yingwei, Qiu Suizheng, Su Guanghui. Development of Prediction Model for Two-phase Flow Regime in Nuclear Reactor Core Based on Artificial Neural Network[J]. Nuclear Power Engineering, 2025, 46(2): 156-163. doi: 10.13832/j.jnpe.2024.090038

Citation:

Ma Yichao, Kong Dexiang, Tian Wenxi, Zhang Jing, Wu Yingwei, Qiu Suizheng, Su Guanghui. Development of Prediction Model for Two-phase Flow Regime in Nuclear Reactor Core Based on Artificial Neural Network[J]. Nuclear Power Engineering, 2025, 46(2): 156-163. doi: 10.13832/j.jnpe.2024.090038

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Development of Prediction Model for Two-phase Flow Regime in Nuclear Reactor Core Based on Artificial Neural Network

doi: 10.13832/j.jnpe.2024.090038

School of Nuclear Science and Technology, Xi’an Jiaotong University, Xi’an, 710049, China

Received Date: 2024-09-16
Rev Recd Date: 2024-10-27

Available Online: 2025-01-15

Publish Date: 2025-04-02

Abstract

Abstract

To fully leverage the increasing experimental data on flow regimes to expand model applicability and improve prediction accuracy, this study collected experimental data, established a training database, and performed data preprocessing. A two-phase flow regime prediction model was developed based on the artificial neural network (ANN) algorithm. The model's prediction accuracy in various flow directions was analyzed and compared with traditional flow regime prediction models. The results show that the new model achieves an average accuracy of 88.56% on the training set and 87.86% on the test set. The proposed model can be directly applied to various operating conditions without causing misclassification of flow regimes in different directions. Compared to the Ishii model, Mandhane model, and Taitel model, the ANN-based model demonstrates superior prediction accuracy. This study provides a novel method for flow regime prediction, and with the continuous updating of training data, the applicability and accuracy of the model can be further improved.
- Nuclear reactor core,
- Two-phase flow regime,
- Machine learning,
- Artificial neural network (ANN)

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References(24)

References

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