Prediction of thermal and hydraulic characteristics of wavy convergent-divergent microchannels with embedded micropins using machine learning - Gonul, Alisan; Dogan, Yahya; Okbaz, Abdulkerim (PERGAMON-ELSEVIER SCIENCE LTD, 2026)
Microchannel heat sinks with non-conventional geometries are promising for thermal management, but their strongly coupled and nonlinear thermo-hydraulic behavior makes predictive modeling difficult. This study presents a comparative assessment of machine-learning models for predicting the Nusselt number and Fanning friction factor in wavy convergent-divergent microchannels with embedded streamlined micropins. A numerical dataset comprising more than 700 samples was generated using three-dimensional conjugate heat transfer simulations over a range of Reynolds numbers and geometric parameters, including wave amplitude, waviness coefficient, and pin height. Five regression models-multilayer perceptron, support vector regression, random forest, gradient boosting regressor, and extreme gradient boosting-were developed and evaluated under a common training, validation, and testing framework. The results show target-dependent model performance. For Nusselt number prediction, support vector regression gave the best performance, with cross-validation and test R2 values of 0.9939 and 0.9970, respectively, and test MAPE below 1%. For friction factor prediction, extreme gradient boosting gave the best performance, with cross-validation and test R2 values of 0.9947 and 0.9972, respectively, and a test MAPE of 2.0844. SHAP analysis showed that the trained models captured physically consistent relationships between flow conditions, geometric parameters, and thermo-hydraulic responses. Compared with the empirical correlations, the data-driven models produced lower prediction errors, with most Nusselt number predictions remaining within the +/- 5% band and most friction factor predictions remaining within the +/- 10% band over the test cases. These results show that machine-learning models can be used as predictive tools for thermo-hydraulic analysis of non-conventional microchannel heat sinks.
Keywords: Wavy microchannels; Micropins; Heat sinks; Heat transfer; Pressure drop; Machine learning; Thermodynamics; Mechanics