Effective dipole model for electrostatic interactions between polarizable spherical particles in particle scale...

Area of Science:

• Physics
• Computational Science
• Materials Science

Background:

• Particle-scale simulations like the Discrete Element Method (DEM) often lack realistic polarization effects for charged particles.
• The standard Coulomb point-charge approximation fails to capture crucial close-distance interactions and polarization phenomena.
• Accurate methods for modeling charged particle interactions are computationally too complex for large-scale DEM simulations.

Purpose of the Study:

• To develop a novel, simplified, yet accurate effective dipole model for simulating electrically charged, polarizable particles.
• To improve the prediction of inter-particle forces in DEM simulations by incorporating realistic polarization.
• To investigate the formation of particle aggregates in DEM simulations using the new model.

Main Methods:

• Development of a closed-form solution for interaction forces between charged polarizable spheres using an effective dipole model.
• Comparison of the proposed dipole model against a rigorous solution for two-particle interactions.
• Dynamic Discrete Element Method (DEM) simulation of a binary particle mixture in a shaker.

Main Results:

• The effective dipole model shows significant improvement over the Coulomb law in predicting interaction forces between charged spherical particles.
• The model's accuracy is validated against rigorous solutions, considering particle size and charge ratios.
• DEM simulations using the effective dipole model successfully predicted the counterintuitive formation of fine-on-coarse aggregates in a charged binary mixture.

Conclusions:

• The proposed effective dipole model offers a computationally efficient and accurate approach for simulating electrically charged, polarizable particles in DEM.
• This advancement enables more realistic modeling of natural and industrial processes involving charged particle dynamics.
• The model's ability to predict aggregate formation highlights its potential for understanding complex particle system behaviors.