Resuspension of Small Particles from Multilayer Deposits in Turbulent Boundary Layers

TL;DR

This paper introduces a hybrid stochastic model for multilayer particle resuspension in turbulent boundary layers, accounting for layer interactions, particle size, adhesion, and force statistics, validated against experimental data.

Contribution

It extends existing models by incorporating multilayer effects, non-Gaussian force statistics, and layer coverage influences into the resuspension process.

Findings

Model accurately predicts multilayer resuspension rates.

Layer thickness and force timescale significantly affect resuspension.

Model aligns well with experimental test results.

Abstract

We present a hybrid stochastic model for the resuspension of micron-size particles from multilayer deposits in a fully-developed turbulent boundary layer. The rate of removal of particles from any given layer depends upon the rate of removal of particles from the layer above which acts as a source of uncovering and exposure of particles to the resuspending flow. The primary resuspension rate constant for an individual particle within a layer is based on the Rock'n'Roll (R'n'R) model using non-Gaussian statistics for the aerodynamic forces acting on the particles (Zhang et al., 2012). The coupled layer equations that describe multilayer resuspension of all the particles in each layer are based on the generic lattice model of Friess & Yadigaroglu (2001) which is extended here to include the influence of layer coverage and particle size distribution. We consider the influence of layer thickness on the resuspension along with the spread of adhesion within layers, and the statistics of non-Gaussian versus Gaussian removal forces including their timescale. Unlike its weak influence on long-term resuspension rates for monolayers, this timescale plays a crucial and influential role in multilayer resuspension. Finally we compare model predictions with those of a large-scale and a mesoscale resuspension test, STORM (Castelo et al., 1999) and BISE (Alloul-Marmor, 2002).