Numerical Study on Nanofluid Turbulent Flow in a Circular Pipe

Abstract

The present work employs water and novel use of air as base fluid. Nanoparticles like newlineAl2O3, and CuO were adopted, in addition to a carbon-based nanoparticle, i.e., newlinegraphene, under the volumetric concentration variation from 0% to 5%. Under constant newlinehear flux and turbulent conditions, the thermo-hydrodynamic nature was observed for newlinethe nanofluids while analysing under different operating conditions. A two-dimensional newlineaxis symmetric circular pipe was taken with Mixture as well as Eulerian multiphase newlinemodels were tested along with RNG k-and#949; turbulence model. newlineThe numerical result show that for both air and water based nanofluids, addition of newlineGraphene, significantly enhanced heat transfer effect as compared to either Al2O3 or newlineCuO. Graphene added nanofluids demanded the least pressure drop followed by Al2O3 newlineand lastly CuO. With higher turbulent kinetic energy, Brownian motion mainly newlinedominated the Graphene motion from wall to pipe center, which not only reduced the newlineeffective viscosity, but also brought down the pipe wall temperature. newlineHeat transfer effectiveness (HTE) and Profit Index (PI) used for evaluation of the newlinethermo-hydrodynamic performance, indicated values of 2 3 and 1.5 2.5, newlinerespectively for Al2O3 and CuO with water base, and values of about 40 120 and 70 newline220 with air base. Graphene-water showed values in the range of 4 5 and 4 10, and newlineGraphene-air jumped to values of 50 200 and 100 400 for the same parameters newlinerespectively. newlineThe Nusselt number correlations were found to be valid for volumetric concentrations newlinein the range of 1% - 5%, Prandtl number from 0.5 2.5, and Reynolds number from newline5,000 9, 00,000, corresponding to air nanofluids, while for water nanofluids, the newlinevalidity was found for nanoparticle volumetric concertation from 0.05% - 5%, newlineReynolds number from 3,000 3,00,000, and Prandtl number from 6 -25. newlineKeywords: Multiphase Flow, Nanofluid, Eulerian, RANS, Machine Learning, newlineTurbulent Flow, Heat Transfer Effectiveness, Graphene, Non-Linear Regression, newlineCorrelation. newline