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 ≈ 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 design-ready surrogates for rapid optimization of magnetically assisted compact heat exchangers.