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HIGH ACCURAY CHF PREDICTION USING NEWEST AI STRATEGIES
ID：2 View Protection：ATTENDEE Updated Time：2024-09-04 21:19:50 Hits：331 Oral Presentation

Start Time：Pending（Asia/Shanghai）

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Abstract
One of the most important and challenging job in the two-phase thermohydraulic filed is the prediction of Critical Heat Flux (CHF) as it serves a critical role in preventing the heater surface form burnout, thus ensuring the safety of the system. As the phenomena itself is so complex, a universal theory to explain all the CHF related topic is still not feasible. Thus, different methods such as empirical or semi-empirical correlations, look-up tables (LUT), CFD methods are proposed to predict CHF. Though people have tried to improve the accuracy of the CHF prediction, traditional methods always face the problem of low accuracy and universalness. AI strategies, which are developed in the recent years, have shown the great potential in accurate prediction of CHF. Compared to traditional methods like LUT, AI can greatly enhance the accuracy, showing the tremendous potential of the method. In this study, we used three different AI strategies to predict CHF, it was found that transformers outweigh among the three methods, achieving the best performance.
 
Keywords
Critical heat flux; Heat transfer; Deep learning; Neural network
Speaker
KAI WANG
Associate Professor THE SUN YA-SEN UNIVERSITY

Submission Author
KAI WANG THE SUN YA-SEN UNIVERSITY
Da Wang Iwhalecloud Co., Ltd. Building B, Yihua Industrial Park, Yuhuatai District, Nanjing City
Xiaoxing Liu 1 Sino-French Institute of Nuclear Engineering and Technology, Sun Yat-Sen University, Zhuhai 519082, China,
Songbai Cheng Harbin Engineering University
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Important Date

  • Conference Date

    Sep 23

    2024

    to

    Sep 25

    2024

  • Sep 24 2024

    Contribution Submission Deadline

  • Sep 25 2024

    Registration deadline

Sponsored By
Harbin Engineering University (HEU)
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