A parametric study of the heat flux partitioning model for nucleate boiling of nanofluids

The dramatic boiling heat transfer performances of nanofluids have been widely attributed to the nanoparticle deposition during the boiling process. The deposited nanoparticles significantly change the microstructures and properties of the heater surface, and hence alter the characteristics of bubble nucleation and departure. Therefore, it is crucial to take into account the effects of nanoparticle deposition when modeling nucleate boiling of nanofluids using the heat flux partitioning (HFP) model (Kurul N., Podowski M.Z., 1990) [1]. In this study, new closure correlations were incorporated for the nucleate boiling parameters including the active site density, the bubble departure diameter and frequency. Parametric studies were performed through 2-D computations to analyze the effects of surface wettability enhancement, the nanoparticle material and size, respectively. The results demonstrated that through appropriate considering the modifications induced by nanoparticle deposition, the HFP model achieved a satisfactory agreement with the experimental data available in the literature, and provided a more feasible and mechanistic approach than the classic Rohsenow correlation for predicting nucleate pool boiling of nanofluids. (C) 2015 Elsevier Masson SAS. All rights reserved.