There is currently a lack of a reliable theory capable of making accurate predictions of the thermal enhancement in nanofluids (with relatively low solid volume fractions). The work described therefore assesses the thermal conductivity of nanoparticle suspensions in fluids using a Lagrangian particle tracking-based computational modelling technique. A 3D, multiphase fluid-solid model is developed which predicts the motion of suspended nanoparticles. The nanofluid is predicted using an Eulerian-Lagrangian hybrid approach with a constant timestep. This technique takes various multiscale forces into consideration in the calculations, whose characteristic scales are quite different, providing for the first time an analysis of all factors affecting the stability and thermal conductivity of nanofluids. The system considered consists of 71 nm diameter Al2O3 ceramic nanoparticles suspended in water, with homogeneous temperature distributions ranging from 25 °C to 85 °C, at various volume fractions between 1% and 5%. The results of the simulations demonstrate the effectiveness of the presented technique, with predictions elucidating the role of Brownian motion, fluid viscous drag, inter-particle collisions and DLVO attraction and repulsion forces on nanofluid stability. Results indicate that aggregated nanoparticles formed in the suspensions, at various particle concentrations, play an important role in the thermal behaviour of the nanofluids. Predictions are in agreement with theoretical and experimental results obtained in related studies. The thermal characteristics of nanofluids are also considered as a function of temperature, system chemistry and time (measured from an initially homogeneously dispersed state). The proven enhancement in the conductivity of fluids affected by the addition of nanoparticles has great potential to assist the development of commercial nanofluid technology aimed at energy efficient and sustainable processes.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)