Unifying same- and different-material particle charging through stochastic scaling

TL;DR

This paper introduces a stochastic scaling model that unifies the prediction of particle charging for both same- and different-material contacts, accurately reproducing complex experimental patterns in large-scale simulations.

Contribution

The paper presents a physics-based stochastic model that predicts particle charging patterns without detailed surface properties, unifying same- and different-material charging in a single framework.

Findings

The model reproduces experimental charging patterns accurately.

It requires less than 0.01% CPU time in large-scale simulations.

The approach does not rely on detailed surface property inputs.

Abstract

Triboelectric charging of insulating particles through contact is critical in diverse physical and engineering processes, from dust storms and volcanic eruptions to industrial powder handling. However, many experiments over the years have consistently revealed counterintuitive charging patterns, including variable impact charge under identical conditions, charge sign reversal with repeated impacts, and bipolar charging of differently sized particles. Existing computational models cannot predict these patterns; they either rely on oversimplified heuristics or require inaccessible detailed surface properties. We present a stochastic scaling model (SSM) for particle charging that unifies same-material (particle-particle) and different-material (particle-wall) charging in a single theoretical framework. The model grounds in a physics-based stochastic closure by the mean, variance, skewness, and minimum impact charge measured in a highly-controlled reference experiment. To test the SSM, we implemented it in an open-source Lagrangian-Eulerian CFD solver. When simulating 300 000 insulating particles transported by turbulent wall-bounded flows, the SSM takes less than 0.01% of the CPU time. By scaling the statistical parameters of the reference impact to each collision, the new model reproduces the complex charging patterns observed in experiments without requiring surface-level first-principles inputs. The SSM offers a physically grounded route to large-scale simulations of electrostatic effects across many fields of particle-laden flows.