Simulation of Enhanced Co2 Mass Transfer of Nanofluid with Lattice Boltzmann Method Coupled Cell Automation Probabilistic Model

Abstract

Nanofluid is a particle suspension composed of liquid and nanoparticles, and have great potential to enhance mass transfer. Most previous studies have only considered the effect of the Brownian motion of nanoparticles on mass transfer. In this study, two-dimensional Lattice Boltzmann method (LBM) combined with the cell automation (CA) probabilistic model is proposed to investigate the mass transfer of CO2 in nanofluids. The model predictions were in good agreement with experimental data, validating the developed mass transfer model. The simulation results indicated that the Brownian motion and grazing effect enhanced the mass transfer in nanofluids, and should be considered simultaneously. Adding 0.1 wt.% SiO2 nanoparticles increased the absorption rate up to 58.3%, and the effective diffusion coefficients reached 5.41 × 10−9 m2s−1. In addition, changing the physical parameters directly affected the Brownian motion and grazing effect, and changed the effective diffusion coefficient of CO2 in the nanofluid. The effective diffusion coefficient decreased with an increase in particle size and increased with an increase in fluid temperature.

Keywords: CFD, Lattice Boltzmann method, Nanofluid, mass transfer, CO2 absorption