Details

Autor(en) / Beteiligte

• Pham, Ngoc H.
• Papavassiliou, Dimitrios V.

Titel

Hydrodynamic effects on the aggregation of nanoparticles in porous media

Ist Teil von

• International journal of heat and mass transfer, 2018-06, Vol.121, p.477-487

Ort / Verlag

Oxford: Elsevier Ltd

Erscheinungsjahr

2018

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• •Simulation of aggregation dynamics of micro- and nanoparticles in porous media.•Multiscale algorithmic approach for Brownian and convection time scale differences in flow simulations.•Predictive model for the calculation of the mean diameter of nanoparticle aggregates. The aggregation of spherical nanoparticles as they propagate through porous media is explored using lattice Boltzmann simulations and tracking of the trajectoties of individual particles. The porous media are modeled as periodic arrays of spheres in different packing configurations. The effects of interparticle interactions on particle aggregation are treated through a single aggregation probability upon particle collision. Fast aggregation is represented by an aggregation probability with values close to one, and slow aggregation is represented by lower aggregation probability. An algorithm that accounts for the different time scales between hydrodynamics and Brownian motion is devised and validated. It is found that primary particle size, initial particle concentration, injection flow rate, and aggregation probability significantly impact the dynamics of the aggregation. Calculations of the transient mean size of the aggregates show that the aggregation rate is high near the entrance of the porous medium, yet dramatically decreases when moving farther downstream. The reason is that the growth of larger aggregates slows the aggregation process. It is also found that the bigger aggregates are formed in the pore space near the pore matrix surface, where particle residence time is long enough to allow the aggregates to grow. An empirical correlation based on measurable parameters is proposed for the prediction of the mean aggregate size.