Numerical Modeling of Nanofluid Thermal Conductivity: The Effect of Nanonetwork on Thermal Transport Behavior

Abstract

Abstract Nanofluids have drawn increasing attention in heat transfer applications due to their anomalous enhancement of the thermophysical properties in contemporary literature. Various studies have shown that the addition of minute concentration of the nanoparticles to a base solvent can yield dramatic enhancement of the effective thermal conductivity. A number of parameters have been reported to affect the level of such enhancement such as size, shape, morphology, concentration, and material properties of the nanoparticles. Many different theoretical models have also been proposed in the past literature for predicting the thermal conductivity of nanofluids under different conditions. In general, these models are based on either simplified static composite model or nanoconvection effect considering the Brownian motion of the nanoparticles. However, a few studies have explored the impact of nanoparticle aggregation on the nanofluid thermal conductivity. In particular, the formation of porous percolation structure by the nanoparticles can alter the effective thermal conductivity of nanofluid substantially. In this study, a two-stage numerical simulation is performed to analyze the thermal transport behavior inside nanofluid considering different levels of percolation network formed by the nanoparticles. Based on the simulation results, an empirical model is developed to predict the effective thermal conductivity of nanofluid as a function of nanoparticle size, concentration, and the permeability of nano-aggregation. The results demonstrated a strong dependence of nanofluid thermal conductivity on the nanocluster density, where a looser nanonetwork can yield a significantly higher level of thermal conductivity enhancement under the same particle size and concentration conditions.