Analysis of nanofluid transport through a wavy channel. (17th December 2017) - Albojamal, Ahmed; Hamzah, Hudhaifa; Haghighi, Arman; Vafai, Kambiz
ABSTRACT: Laminar forced convection of heat transfer and pressure drop of Al 2 O 3 and CuO /water nanofluids flow through a horizontal tube and wavy channel under constant wall temperature boundary condition is numerically investigated. Two different models were employed in our study: single phase (homogenous and dispersion) and two phase (Lagrangian–Eulerian model or discrete-phase model (DPM) and the mixture). The effects of various parameters, such as particle concentration, particle diameter, particle type, constant or temperature-dependent properties, wave amplitude, Reynolds number and Peclet number on the thermal, and flow field of the Nanofluids are analyzed. Our results revealed that variable properties assumption play a dominant role in horizontal tubes and provide better predictions for the heat transfer enhancement. The difference between constant and variable properties becomes insignificant and can be ignored in wavy channel due to the high mixing and generated recirculation zones, whereas the difference between the DPM and the single-phase variable properties diminish as Peclet number and volume fraction increases. However, dispersion model shows an excellent agreement with the experimental data; the absence of the reference values for the adjustable factor C d in the open literature put it in a questionable position. Therefore, DPM and homogenous single-phase model with well-chosen thermal conductivity and viscosity correlations can be considered as an accurate way and more dependable in nanofluid simulations especially the homogenous single-phase model because it requires less time, CPU, and memory usage. As expected, it is found that the heat transfer increases as the Reynolds number and particle volume fraction increases, but it is accompanied by a higher pressure drop. The obtained results have been successfully validated and compared with the experimental and numerical data available in the literature.