Experimental study and ANFIS modelling of the thermophysical properties and efficacy of GNP-Al2O3 hybrid nanofluids of different concentrations and temperatures

Abstract

Abstract This study delves into an extensive investigation of the thermophysical properties and heat transfer efficacy of a hybrid nanofluid incorporating graphene nanoplatelets and γ-Al2O3 nanoparticles dispersed in deionised water. The nanofluids were characterised for their viscosity (µ), thermal conductivity (λ), and electrical conductivity (σ) over a 15–40 °C temperature range for varying nanoparticle loading (0.1–0.4 vol%). The experimental results revealed notable enhancements in µ, λ, and σ with increasing nanoparticle concentration, while µ decreased at elevated temperatures as λ and σ increased. At the highest concentration (0.4 vol%), µ increased by 21.74%, while λ and σ exhibited peak enhancements of 17.82% and 393.36% at 40 °C. An Adaptive Neuro-fuzzy Inference System (ANFIS) model was devised to enhance predictive precision by meticulously optimising the number of membership functions (MFs) and input MF type. The ANFIS architecture that exhibited the most remarkable agreement with the experimental data for µ, λ, and σ was found to utilise the Product of Sigmas, Difference of Sigmas, and Generalized Bell MFs, respectively, with corresponding input MF numbers being 2–3, 3–2, and 3–2. The optimal ANFIS model for µ, λ, and σ exhibits a higher prediction accuracy with an R2 value of 0.99965, 0.99424 and 0.99995, respectively. The Figure of Merit analysis using Mouromtseff Number identified an optimal nanoparticle concentration range of 0.1–0.2 vol% for enhanced heat transfer performance with a reasonable µ increase. This range guides practitioners in utilising hybrid nanofluids effectively while managing potential drawbacks.

Keywords

• Hybrid nanofluids
• Graphene nanoplatelets
• Al2O3
• Thermophysical properties
• Figure-of-merit
• Adaptive neuro-fuzzy inference system