What question did this study set out to answer?

This research aims to develop a hybrid modelling framework that enhances predictive accuracy and extrapolation for heat transfer systems.

February 2, 2026Open Access

Hybrid modelling of heat transfer systems: Combining physics-based and data-driven approaches for improved prediction and extrapolation

Key Points

This research aims to develop a hybrid modelling framework that enhances predictive accuracy and extrapolation for heat transfer systems.
Developed a hybrid modelling framework combining mechanistic models and machine learning algorithms.
Applied the framework to predict the condensation heat transfer coefficient in a microfin tube.
Utilized a laboratory-scale dataset containing 5708 datapoints for validation of the framework.
Assessed the model's performance in prediction and extrapolation against pure mechanistic and pure machine learning models.
Hybrid models achieved approximately 60% more accurate predictions compared to mechanistic models while interpolating.
Hybrid models showed 25% improvement over machine learning models in predictive accuracy.
While extrapolating, hybrid models performed approximately 50% better than pure machine learning models.
Hybrid models outperformed mechanistic models by about 27% in extrapolation accuracy.

Abstract

Hybrid machine learning-assisted modelling techniques have gained increasing attention recently in many engineering fields. This is due to the challenges associated with pure first-principles and data-driven models, as the former requires deep phenomenological understanding and might become infeasible to describe a complex system with, and the latter needs extensive high-quality data and, more importantly, extrapolates poorly compared to its first principles counterparts. The integration of the two techniques in a framework will result in an integrated approach that benefits from the two realms by strengthening extrapolation capabilities, higher prediction accuracy, and less data demanding and more data-efficient. In this study, a systematic hybrid modelling framework is developed, allowing for the integration of mechanistic models and machine learning algorithms in parallel and series for modelling heat transfer systems to predict a desired target variable, as long as the system is not of a dynamic nature. The framework is developed according to a previous study that enabled the use of machine learning models for such systems. The application of the hybrid modelling framework in this study is demonstrated on the prediction of the condensation heat transfer coefficient in a microfin tube. A laboratory-scale dataset of 5708 datapoints is used for the validation of the developed framework. The validation of the model has been carried out in two different scenarios, both assessing the general prediction and extrapolation capabilities of the developed models in comparison with pure mechanistic and pure machine learning models. The hybrid models, series and parallel, outperform the mechanistic model by approximately 60% more accurate predictions and the machine learning model by 25%, while interpolating. More importantly, while extrapolating, the hybrid models showed approximately 50% more accurate predictions compared to pure machine learning and 27% more accurate compared to the mechanistic model.

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Cite This Study

Nazemzadeh et al. (Thu,) studied this question.

synapsesocial.com/papers/6980fd3cc1c9540dea80efc0 https://doi.org/https://doi.org/10.1016/j.aitf.2026.100030

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