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 volume%). 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 volume% for enhanced heat transfer performance with a reasonable µ increase. This range guides practitioners
in utilising hybrid nanofluids effectively while managing potential drawbacks.
