Physics-informed machine learning for enhanced prediction of condensation heat transfer

Developing a universal model for predicting condensation heat transfer coefficients remains challenging, particularly for steam–non-condensable gas mixtures, owing to the intricate nonlinear interactions between multiphase flow, heat, and mass transfer phenomena. Data-driven machine learning (ML) shows promise in efficiently and accurately predicting condensation heat transfer coefficients. Research has employed various ML methods—multilayer perceptron neural networks, convolutional-neural-network–based DenseNet, backpropagation neural networks, etc.—to investigate steam condensation with non-condensable gases. However, these exhibit limited extrapolation ability and heavily rely on data quantity owing to their black-box nature. This study proposes a physics-informed ML model that combines physical constraints derived from the modified Nusselt model with conventional data-driven ML techniques. The model's predictive performance is evaluated using a comprehensive database (879 datapoints from 13 studies). A physics-constrained and eight data-driven ML methods are assessed. The results reveal that the physics-constrained approach combined with XGBoost significantly outperforms conventional ML methods on extrapolation datasets (199 datapoints from 3 studies), achieving a mean absolute percentage error of 11.22 %, which is approximately half that of the best-performing fully data-driven model at 21.63 %. The model demonstrates consistent and reliable performance across diverse datasets, making it an effective tool for predicting heat transfer coefficients in steam–non-condensable gas mixtures. By deepening the understanding of the underlying physical processes, the proposed model supports the development of precise and efficient engineering solutions for condensation heat transfer.