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Thermal conductivity enhancement of nanofluid by adding multiwalled carbon nanotubes: Characterization and numerical modeling patterns
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DOI：10.1002/mma.6466.png)
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Abstract
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Nanofluid is divided in two major section, mono nanofluid (MN) and hybrid nanofluid (HN). MN is created when a solid nanoparticle disperses in a fluid, whereas HN has more than one solid nanomaterial. In this research, iron (III) oxide (Fe3O4) is MN, and Fe3O4 plus multiwalled carbon nanotube (MWCNT) is HN, whereas both are mixed and dispersed into the water basefluid. Thermal conductivity (TC) of Fe3O4/water and MWCNT/Fe3O4/water was measured after preparation and numerical model performed on the resulted data. After that, field emission scanning electron microscope (FESEM) was studied for microstructural observation of nanoparticles. MN and HN TC were studied at temperature ranges of 25 to 50 degrees C and volume fractions of 0.2% to 1.0%. For MN and HN, thermal conductivity enhancement (TCE) of 32.76% and 33.23% was measured at 50 degrees C temperature-1.0% volume fraction, individually. Different correlations have been calculated for numerical modeling, with R 2 = 0.9. Deviation of 0.6007% and 0.6096% was calculated for given correlations for MN and HN individually. Deviation of 0.5862% and 0.6057% was calculated for trained models, for MN and HN individually. Thus, by adding MWCNT to Fe3O4-H2O nanofluid, TC is enhanced 0.47%, and this HN has agreeable heat transfer potential.
Keywords:
artificial neural network
MWCNT
thermal conductivity
numerical modeling
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