Developing correlation for prediction of departure from nucleate boiling heat flux in subcooled flow boiling using artificial neural network-aided framework

Nguyen, T.-B.; 廣瀬 意育  ; 佐藤 聡 ; 安部 諭  ; 柴本 泰照 

Nguyen, T.-B.; Hirose, Yoshiyasu; Satou, Akira; Abe, Satoshi; Shibamoto, Yasuteru

Departure from nucleate boiling (DNB) in subcooled flow boiling leads to an abrupt rise in the heat transfer surface temperature in a short duration of time. In light water reactor (LWR), knowledge about DNB heat flux is essential to avoid failure in the heating devices. Due to the complexity of the phenomenon, an accurate prediction method for DNB heat flux remain a challenging issue. Empirical correlations, mechanistic models and AI-aided models for predicting DNB heat flux found in open literature continue to exhibit certain limitations such as the disagreements on the DNB mechanism or the lack of transparency inherent in AI-aided models. In this study, artificial neural network (ANN) was utilized to analyze the inter-relationship between DNB heat flux and six influential parameters gathered from available resources. After that, a minimal number of the most critical dimensionless quantities were extracted to develop a new correlation. It is proven that the proposed model has superior features such as robustness, simplicity, applicability over a wide range of operating conditions and independent of thermal-hydraulic conditions.