... MPa, mass flux from 25 to 2750 kg/m 2 s, and inlet subcooling from 1 to 70 °C. Two machine learning (ML) models, based on random forest (RF) and gradient boosted decision tree (GBDT), are trained and validated to predict wall temperatures in post-CHF flow regimes. The trained ML models demonstrate...

... algorithm (GA) has been used to determine the coefficients of heat transfer correlations by separation of total heat transfer coefficient without any information of measured wall temperatures. It is concluded that the GA-based separated heat transfer Nusselt number provides a good agreement...