Turbulent convective heat transfer of silica oxide nanofluid through corrugated channels: An experimental and numerical study

Combining a corrugated surface and nanofluids technologies have caused attracted significant interest to develop the ability of compact heat exchangers in order to produce more efficient and reliable thermal systems. In this paper, the forced convective turbulent flow of SiO2-water nanofluid through...

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Bibliographic Details
Main Authors: Ajeel, R.K., Salim, W.S.-I., Sopian, K., Yusoff, M.Z., Hasnan, K., Ibrahim, A., Al-Waeli, A.H.A.
Format: Article
Language:English
Published: 2020
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Summary:Combining a corrugated surface and nanofluids technologies have caused attracted significant interest to develop the ability of compact heat exchangers in order to produce more efficient and reliable thermal systems. In this paper, the forced convective turbulent flow of SiO2-water nanofluid through different corrugated channels is studied numerically and experimentally. All studies are performed for the straight channel (SC) and different two corrugated channels, namely semicircle corrugated channel (SCC) and trapezoidal corrugated channel (TCC) over Reynolds number ranges of 10000–30000. SiO2 nanoparticles suspended in distilled water with two particle volume fractions (1% and 2%) were successfully prepared and tested. Numerically, the discussion and analysis on heat transfer and flow characteristics which including velocity, isotherms contours, turbulence kinetic energy, vortices magnitude are provided. The results show that the corrugation profile has a significant impact on heat transfer enhancement compared to the straight profile. Also, silica nanofluid shows a better heat transfer in comparison with the base fluid. The new style of trapezoidal corrugated channel offers the best heat transfer enhancement. This indicated that this geometry with silica nanofluid can improve the heat transfer significantly with a reasonable increase in pressure drop. The results for the numerical outcomes and experimental data are in good agreement. © 2019 Elsevier Ltd