@inproceedings{d42caa277a4f442a8bd7e2a99d5722d8,
title = "RESEARCH ON CHF MODEL BASED ON DEEP LEARNING ALGORITHM",
abstract = "In the two-phase flow model, the accurate prediction of Critical Heat Flux (CHF) is related to the safety margin of reactor design. In this paper, the CHF calculation model is developed based on the Artificial Neural Network (ANN) and random forest model, the accuracy of the model is verified, and the most suitable model for application is selected. The selected CHF model was coupled with RELAP5, and the coupled program was verified based on the Thermal-Hydraulic Test Facility of the Oak Ridge National Laboratory (ORNL-THTF). The results show that the CHF model based on the machine learning algorithm has good accuracy in calculation, and RELPA5 with the new CHF model is closer to the experimental data in wall temperature calculation. The purpose of optimizing the program is achieved.",
keywords = "ANN, CHF Model, RELAP5",
author = "Dexiang Kong and Yichao Ma and Jing Zhang and Mingjun Wang and Yingwei Wu and Guanghui Su and Suizheng Qiu and Wenxi Tian",
note = "Publisher Copyright: {\textcopyright} 2023 by JSME.; 30th International Conference on Nuclear Engineering, ICONE 2023 ; Conference date: 21-05-2023 Through 26-05-2023",
year = "2023",
language = "英语",
isbn = "9784888982566",
series = "International Conference on Nuclear Engineering, Proceedings, ICONE",
publisher = "American Society of Mechanical Engineers (ASME)",
booktitle = "Proceedings of the 30th International Conference on Nuclear Engineering {"}Nuclear, Thermal, and Renewables",
}