Numerical simulation of aggregation effect on nanofluids thermal conductivity using the lattice Boltzmann method

Abstract

The standard enhancement in nanofluids thermal conductivity due to the addition of nanoparticles is well understood. Despite this, the reason behind observed anomalous increases is still controversial. Limitations in nano-scale experimental observations would make it even harder to approach into this topic. To address this issue, researchers have proposed many different macroscopic (continuum-based)/microscopic (molecular scale) numerical schemes as an alternative for experimental investigations. However, the overall thermal effect of suspended nano-scale particles cannot be observed in neither macroscopic nor microscopic scale due to collective interrelated behaviors such as nanoparticles aggregation. In this paper, a mesoscopic approach, Lattice Boltzmann method (LBM), aims to consider microscopic phenomena in a broader context (mesoscopic scale), been implemented to investigate the nanoparticles aggregation as a probable working mechanism behind the anomalous increase in nanofluids thermal conductivity. The stochastic and dynamic nature of nanoparticles aggregation is captured through generation of fractal random microstructures. The effects of size, shape and distribution regime of aggregates are studied and optimum values are calculated. The results indicate that the aggregation can anomalously enhance nanofluids effective thermal conductivity (ETC). The LBM results are found to be in great agreement with the available numerical/experimental data in the literature.