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Prediction of the dynamic viscosity, electrical conductivity, thermal conductivity, and pH of Fe₃O₄/TiO₂ hybrid nanofluids using a proposed framework with a machine learning method
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DOI：10.1016/j.ceja.2026.101398.png)
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Abstract
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• This study establishes a predictive modeling framework to quantify the coupled thermophysical and physicochemical properties of Fe₃O₄/TiO₂ hybrid nanofluids. • Dynamic viscosity, electrical conductivity, thermal conductivity, and pH were simultaneously predicted as functions of nanoparticle volume fraction and temperature. • overall mean square error values of 5.68754×10⁻⁴ for dynamic viscosity, 1.92314×10⁻⁴ for electrical conductivity, 7.02043×10⁻⁷ for thermal conductivity, and 5.54188×10⁻³ for pH. • sensitivity analysis revealed that dynamic viscosity exhibits the highest responsiveness, with a maximum deviation of 14.947% under input perturbation.
Keywords:
Thermophysical properties
Fe₃O₄/TiO₂ hybrid nanofluid
Artificial neural networks
Energy efficiency
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7.1
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1.4K
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3.9K
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