A new semi-analytical model for effective thermal conductivity of nanofluids

A new theoretical model for thermal conductivity of nanofluids is developed incorporating effective medium theory, interfacial layer, particle aggregation and Brownian motion-induced convection from multiple nanoparticles/aggregates. The predicated result using aggregate size, which represents the particle size in the actual condition of nanofluids, fits well with the experimental data for water-, R113- and ethylene glycol (EG)-based nanofluids. The present model also gives much better predictions compared to the existing models. A parametric analysis, particularly particle aggregation, is conducted to investigate the dependence of effective thermal conductivity of nanofluids on the properties of nanoparticles and fluid. Aggregation is the main factor responsible for thermal conductivity enhancement. The dynamic contribution of Brownian motion on thermal conductivity enhancement is surpassed by that of static mechanisms, particularly at high volume fraction. Predication also indicated that the viscosity increases faster than the thermal conductivity, causing the highly aggregated nanofluids to become unfavourable, especially for d_f = 1.8.