Lattice-boltzmann method with dynamic grid refinement for simulating particle deposition on a single fibre

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Abstract

In this work a numerical approach to predict the deposition behaviour of nano-scale particles on the surface of a single fibre by resolving the resulting dendrite-like particle structures in detail is presented. The gas flow simulation is carried out by a two-dimensional Lattice-Boltzmann method, which is coupled with a Lagrangian approach for the particle motion. To decrease calculation time and system requirements the Lattice-Boltzmann model is extended to allow for local grid refinement. Because of the a priori unknown location of deposition, the simulation procedure starts on a coarse mesh which is then locally refined in a fully adaptive way in regions of accumulated particles. After each deposition the fluid flow is recalculated in order to resolve the coupling of the flow with the growing particle structures correctly. For the purpose of avoiding unphysical blocking of flow by growing particle dendrites the Lattice-Boltzmann method is extended to permeable cells in these regions using the Brinkmann equation. This extended deposition model is compared to simpler approaches, where the deposit has no retroaction on the flow or is treated as a solid structure. It is clear that the permeable model is most realistic and allows considering the particle deposition on a fibre as two-dimensional problem. Comprehensive simulations were conducted for analysing the importance of different parameters, i.e. free-stream velocity and particle diameter on the deposit structure. The results of this sensitivity analysis agree qualitatively well with former published numerical and experimental results. Finally the structure of the particle deposit was quantitatively characterised by using a modified fractal dimension.

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Schomburg, H., Dietzel, M., Sommerfeld, M., Michaelis, B., & Teike, G. (2013). Lattice-boltzmann method with dynamic grid refinement for simulating particle deposition on a single fibre. Journal of Computational Multiphase Flows, 5(1), 1–26. https://doi.org/10.1260/1757-482X.5.1.1

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