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

TL;DR

This paper introduces triboFoam, an open-source simulation tool for studying triboelectric charging in turbulent flows, demonstrating how Reynolds number influences particle charging and providing empirical predictions for industrial applications.

Contribution

The study develops and validates triboFoam, a new open-source solver, and investigates the impact of Reynolds number on particle charging in turbulent flows, including an empirical correlation for charging rates.

Findings

Higher Reynolds numbers increase near-wall particle concentrations.

Enhanced turbulent fluctuations lead to higher charging rates.

Empirical correlation predicts charging rate based on Reynolds number and particle size.

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 $Re_\tau = 180$. 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 $Re_\tau = 550$. 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.