Tree-based Ensemble Learning Models for Wall Temperature Predictions in Post-CHF Flow Regimes at Subcooled and Low-quality Conditions

Accurately predicting post-critical heat flux (CHF) heat transfer is an important but challenging task in water-cooled reactor design and safety analysis. Numerous post-CHF heat transfer correlations have been developed in the literature but are only applicable to relatively narrow ranges of flow conditions. In this paper, a large number of experimental data are collected and summarized from the literature for steady-state subcooled and low-quality film boiling regimes with water as the working fluid in tubular test sections. A Low-quality Water Film Boiling (LWFB) database is consolidated with a total of 22,813 experimental data points, which cover a wide flow range of the system pressure from 0.1 to 9.0 MPa, mass flux from 25 to 2,750 kg/m2-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 significantly improved accuracies compared to conventional empirical correlations. To further evaluate the performance of these two ML models from a statistical perspective, three criteria are investigated, and three metrics are calculated to quantitatively assess the accuracy of these two ML models. For the full LWFB database, the RMSEs between the measured and predicted wall temperatures by the GBDT and RF models are 5.7% and 6.2%, respectively, confirming the accuracy of the two ML models.