Experimental and ANN-Based Evaluation of Water-Based Al 2 O 3, TiO 2, and CuO Nanofluids for Enhanced Engine Cooling Performance

Gadisa Sufe (), Zbigniew J. Sroka and Monika Magdziak-Tokłowicz
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Gadisa Sufe: Faculty of Mechanical Engineering, Wrocław University of Science and Technology, 50-370 Wroclaw, Poland
Zbigniew J. Sroka: Faculty of Mechanical Engineering, Wrocław University of Science and Technology, 50-370 Wroclaw, Poland
Monika Magdziak-Tokłowicz: Faculty of Mechanical Engineering, Wrocław University of Science and Technology, 50-370 Wroclaw, Poland

Energies, 2025, vol. 18, issue 18, 1-24

Abstract: This study presents an integrated experimental and computational investigation into the thermal and hydraulic performance of three oxide-based nanofluids: aluminum oxide (Al 2 O 3 ), titanium dioxide (TiO 2 ), and copper oxide (CuO) for advanced engine cooling applications. A custom-built test rig was used to assess nanofluid behavior under varying flow rates, nanoparticle volume fractions, and temperature gradients, replicating realistic engine conditions. According to the results, at ideal concentrations, CuO nanofluids continuously demonstrate better heat transfer properties, outperforming TiO 2 by up to 15% and AlO 3 by 7%. However, performance plateaus beyond 1.5% volume fraction due to increased viscosity and pressure drop. A multilayer feedforward artificial neural network (ANN) model was developed to predict convective heat transfer coefficients and friction factors based on experimental inputs, achieving a mean absolute percentage error below 5% and a coefficient of determination (R 2 ) exceeding 0.98. The ANN demonstrated robust generalization across varying operating conditions and nanoparticle types, confirming its utility for surrogate modeling and optimization. This work is distinguished by its dual focus on thermal efficiency and hydraulic stability, as well as its use of data-driven modeling validated by empirical results. The findings provide actionable insights for thermal management system design in internal combustion, hybrid, and electric vehicles, where efficient, compact, and reliable cooling solutions are increasingly vital. The study advances the practical application of nanofluids by offering a comparative, ANN-validated framework that bridges the gap between lab-scale performance and real-world automotive cooling demands.

Keywords: nanofluids; engine cooling; aluminum oxide (Al 2 O 3 ); copper oxide (CuO); titanium dioxide (TiO 2 ); artificial neural network (ANN) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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