Effect of Reynolds number on triboelectric particle charging in turbulent channel flow

TL;DR

This study introduces triboFoam, an OpenFOAM-based CFD–DEM tool for simulating triboelectric charging in particle-laden turbulent flows. The authors validate the solver against DNS at $Re_\tau=180$ and extend the analysis to LES up to $Re_\tau=550$, examining 25–100 μm PMMA-like particles and multiple coupling scenarios. They implement two charging models, condenser and stochastic scaling (SSM), and compare 4-way coupling effects on near-wall turbophoresis and collision statistics, revealing that higher Reynolds numbers generally enhance charging, with small particles showing the strongest response. To enable practical predictions, they derive two symbolic-regression-based correlations linking the charging rate to $Re_\tau$ and particle diameter, highlighting non-monotonic trends at high Reynolds numbers. The work provides a robust, open-source framework for studying electrostatic phenomena in complex turbulent geometries and offers actionable guidance for industrial safety and process optimization.

Abstract

Triboelectric charging in particle-laden flows is a complex interplay of fluid and particle dynamics, collision mechanics, and electrostatics. In this study, we introduce triboFoam, an open-source solver built on the OpenFOAM framework, designed to simulate triboelectric charging in particle-laden turbulent flows. We validate triboFoam using Direct Numerical Simulations (DNS) of a fully developed turbulent channel flow at a friction Reynolds number of $Re_τ= 180$. The results demonstrate good agreement with DNS data for particle concentration profiles and charge distributions. Then, we investigate the influence of Reynolds number on particle distribution and charging behaviour using Large-Eddy Simulations (LES) at varying friction Reynolds numbers up to $Re_τ= 550$. Our findings reveal that higher Reynolds numbers lead to increased near-wall particle concentrations and enhanced charging rates, attributed to intensified turbulent fluctuations and elevated impact velocities. Finally, an empirical correlation is proposed to predict the average particle charging rate as a function of Reynolds number and particle diameter. With this work, we provide a tool for simulating triboelectric charging in complex geometries and turbulent flows, advancing the understanding of electrostatic phenomena in particle-laden systems. The empirical correlation offers practical insights for predicting charging behaviour in industrial applications and thus can contribute to improved safety and efficiency in processes involving particulate matter.

Figures (35)

• Figure 1:
• Figure 2:
• Figure 4: Condenser model
• Figure 5: SSM
• Figure 7: Relative particle density $\overline{C}_\text{rel}$ plotted over wall-normal coordinate $y^+$ for uncharged particles of different sizes for DNS of $\hbox{Re}_\tau=180$. The coupling between the particles and the fluid is varied between 1-way, 2-way, and 4-way. The particle concentration profiles are based on the particle centre.