Stability, thermophysical properties, forced convective heat transfer, entropy minimization and exergy performance of a novel hybrid nanofluid: Experimental study

• Main Authors: Kanti P.K., Vicki Wanatasanappan V., Mahjoub Said N., Sharma K.V.
• Other Authors: 57216493630
• Format: Article
• Published: Elsevier B.V. 2025
• Subjects: Entropy; Graphene; Heat convection; Heat flux; Nanocomposites; Nanofluidics; Nanoparticles; Reynolds number; Thermal conductivity; Entropy generation; Exergy Analysis; Graphene oxides; Irreversibility; Nanofluids; Nanoparticle concentrations; Performance; Performance indices; Red mud; Red mud nanofluid; Exergy

This research explores how incorporating graphene oxide (GO) into red mud (RM) creates a superior nanomaterial for heat transfer applications. RM's inherent stability and thermal conductivity (TC), stemming from its metal oxide composition, are further amplified by this hybrid nano-composite. The study primarily investigates the thermal performance of water-based RM mono nanofluid and hybrid RM + GO (50:50) nanofluids (HNFs) at nanoparticle concentrations of 0.1?0.75 vol%. An experimental setup consisting of a copper tube under turbulent flow conditions with a constant heat flux and a bulk fluid temperature of 60 �C was used to test the performance of HNF. Various techniques are used to characterize the nanoparticles (NPs), and evaluate the thermophysical properties of nanofluids. The experimental data reveal that the heat transfer coefficient (HTC) increases with higher inlet fluid velocity and nanoparticle concentrations. Notably, the HNF demonstrates a significant improvement of 47.2 % in Nusselt number (Nu) and a 13.6 % rise in pressure drop (?p) compared to base fluid, at 0.75 vol%. The least entropy generation number (EGN) is obtained for the HNF (0.0049) compared to the RM NF (0.00583) at a concentration of 0.75 vol%. The exergy efficiency improves with an increase in concentration and Reynolds number (Re). Additionally, the study identifies the performance index (PI) of NFs and modelled correlations for estimating the Nu and friction factor (f) within the investigated concentration range. ? 2024 Elsevier B.V.