Presentation Details
Research on CHF look-up table prediction based on deep learning
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Updated Time：2024-09-08 17:36:03 Hits：332
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Abstract
Abstract: In nuclear fuel rod bundles and steam generators, the heating surface may fail once the wall surface exceeds the CHF, the mechanism of CHF generation is complex, and there is no single theory or equation that can be applied to all CHF conditions. The CHF lookup table is basically a normalized database that predicts CHF as a function of coolant pressure, mass flux, and thermodynamic mass. The deep learning model can effectively predict CHF, and its prediction performance is simpler and more accurate than traditional relational fitting. Based on the data of the CHF lookup table, we use BP neural network, genetic algorithm BP neural network, and one-dimensional convolutional neural network to predict the predicted value of CHF within the measurement range, and compares it with several common empirical relationships. The results show that the prediction performance of the three deep learning models is improved to varying degrees compared with the empirical relationship. BP neural network is the fastest to train; The genetic algorithm -BP neural network has the best prediction performance, with MAE=1.490%, MSE=0.03%, and R²=0.976, but the training time is too long and requires a lot of resources. The prediction performance of the one-dimensional convolutional neural network is slightly lower than that of the GA-BP neural network, with MAE=1.574%, MSE=0.06%, and R²=0.968, and the training time is comparable to that of the BP neural network, which occupies less resources. The research results provide a new way for the prediction research of CHF look-up table.
Keywords
CHF,One-dimensional convolutional neural network,BP neural network,Algorithm-BP neural network,Deep learning
Speaker
Kai Wu
China Nuclear Power Technology Research instituteSubmission Author
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Important Date
-
Conference Date
Sep 23
2024
toSep 25
2024
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Sep 24 2024
Contribution Submission Deadline
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Sep 25 2024
Registration deadline
Sponsored By
Harbin Engineering University (HEU)
Contact Information
- Prof. WANG Xiang
- xi******@hrbeu.edu.cn
Previous Conferences
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2016-09-26 China Chengdu,China
8th International Symposium on Symbiotic Nuclear Power Systems for 21st Century -
2016-09-26 China Chengdu,China
8th International Symposium on Symbiotic Nuclear Power Systems for 21st Century -
2010-08-23 China 哈尔滨市
The 3rd International Symposium on Symbiotic Nuclear Power Systems for 21st Century (ISSNP2010)
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