The
minimum film boiling (MFB) temperature, a crucial industrial
design parameter for nuclear reactors, cryogenic milling, and grinding,
refers to the lowest sustainable temperature at which stable film
boiling occurs. Despite the development of many thermodynamic and
hydrodynamic models, a universal model for predicting the MFB temperature
is still required. In recent years, machine learning (ML) methods
have been shown to outperform previous correlations in predicting
the MFB temperature. However, these ML methods still have limitations
in interpretability and the inclusion of key parameters, such as surface
roughness, in the database. To address these issues, this study applies
six ML models and a data interpretation method (SHapley Additive exPlanations,
SHAP) to investigate MFB temperature. The extreme gradient boosting
(XGBoost) model performs the best among these ML models, with an MAE
of 2.6% for predicting MFB temperature without considering surface
roughness, significantly outperforming the existing correlations.
To account for surface roughness, a reduced database excluding data
points without roughness values was used to train new XGBoost models.
To fully use the removed data, a strategy is developed where normalized
roughness and the MFB temperature predicted by the XGBoost model trained
with the complete database excluding roughness are used as input variables.
This approach improves the performance and generalization capabilities
under insufficient surface roughness data sources by transferring
the knowledge acquired from the complete database to the ML model
developed by using the reduced database. The results show that the
contributions of input variables and the physical variation trend
of the XGBoost models are consistent with previous experimental results
and theories.