Comparative machine learning prediction study of hybrid nanofluid flow in a magnetized dimpled tube

Abstract

This study experimentally examines thermo-hydraulic performance of mono and hybrid nanofluids (Fe3O4/H2O, Cu/H2O, and Fe3O4-Cu/H2O) flowing through smooth (ST) and dimpled tubes (DT) under laminar conditions (Re = 1131-2102) with constant heat flux. A total of 95 cases were tested while a constant direct magnetic field (MF = 0.03, 0.16, 0.3 T) was applied via twin coils; performance was assessed using the Heat Convection Ratio (HCR), Pressure Ratio (PR), and Performance Evaluation Criterion (PEC). Baseline validation against Shah-London and Hagen-Poiseuille correlations showed deviations <= 5.85% (Nu) and <= 4.11% (f). DTs enhanced heat transfer substantially: with Fe3O4/H2O, HCR in DT exceeded ST by up to 43.2% at Re = 2102, while pressure penalties remained moderate. MF strength critically shaped outcomes: 0.16 T consistently improved HCR and yielded the best thermo-hydraulic balance (higher PEC), whereas 0.3 T increased PR and could depress PEC below unity, especially in ST. Data-driven models (Linear, Polynomial, XGBoost, ANN) were trained to predict HCR, PR, and PEC. Polynomial Regression achieved the highest accuracy for HCR and PR on the test set (R2 approximate to 0.99), while XGBoost provided slightly superior PEC predictions. SHAP analyses identified MF strength and dimple geometry as the dominant drivers across targets, with velocity/Re effects modulating performance. The results demonstrate that DTs combined with low-to-moderate MF intensities and Fe3O4-based nanofluids deliver practical heat-transfer gains with acceptable pumping costs; the accompanying predictive models furnish designready surrogates for rapid optimization of magnetically assisted compact heat exchangers.