Nanofluids have become a point of intense interest for its usability in sectors where convective heat transfer is a requirement. Whereas knowing the overall thermal transport characteristics of nanofluids is the key for their proper utilisation, the domain of nanofluids turbulent convective heat transfer is still heavily understudied, where conducting a parametric study on their heat transferring behaviour along with assessing the effect of boundary conditions on their heat transfer enhancement and the available CFD models’ efficiency to account for nanoparticle size are vital necessities. In this study, highly turbulent flow of nanofluids inside a circular pipe under constant wall temperature has been simulated using the Mixture model. Correlations between all the parameters related to nanofluids turbulent convective heat transfer have been established and the impact of variable temperature boundary condition on nanofluids heat transfer enhancement has been investigated. In addition, Mixture models’ ability to assess nanoparticle size variation on heat transfer of nanofluids has been shown. Results suggest that nanofluids heat transfer is dominated by the amount of nanoparticle concentration present in the base fluid when Reynolds number is kept constant. Also, for a certain particle concentration, intensification of heat transfer is guided by the degree of turbulence. The findings also depict that nanofluids heat transferring capability is independent of the temperature boundary conditions used and Mixture model is unable to assess the change in heat transfer due to variation in nanoparticle size.
Because of complications and cost of experimental studies, simulating heat transfer of nanofluids using the methods of computational fluid dynamics (CFD) has become a reliable approach to work with them. As Mixture model remains as one of the most heavily used CFD models to examine
the heat transfer enhancement of nanofluids according to literature, finding out the range of nanofluid configurations for which the Mixture model is able to provide satisfactory results is a necessity. In this study, turbulent flow of water-Al2O3 inside a circular pipe
under uniform wall temperature has been simulated in order to find out the conditions for Mixture model to yield reliable results in terms of predicting heat transfer enhancement of nanofluids. Along with depicting significant increase in heat transfer with particle concentration hike, the
results suggested that Mixture model predicts heat transfer enhancement the most accurately for nanoparticle concentration of around 3% with an average discrepancy of less than 1% from experimental data, though the results for particle concentration range of 2.5% to 3.5% are quite satisfactory
yielding average error lower than 8%.
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