Project Overview

Airborne particulate matter poses significant environmental and public health challenges. Electrostatic filtration provides an energy-efficient approach for capturing fine particles; however, the underlying transport mechanisms are governed by complex interactions between fluid flow, electric fields, and particle dynamics.

This project focused on developing a high-fidelity multiphysics computational framework capable of simulating these coupled physical phenomena. The framework enables quantitative analysis of charged particle transport, deposition, and collection around electrostatically charged spherical collectors under different operating conditions.

Research Objectives

Develop a coupled CFD–electrostatics numerical framework for aerosol transport.
Investigate the influence of non-uniform electric fields on charged particle motion.
Quantify particle collision, rebound, and collection efficiencies.
Study the effects of particle size, flow velocity, applied voltage, and particle charge.
Generate high-fidelity datasets for machine learning and engineering optimization.

Methodology

A fully coupled multiphysics model was developed in COMSOL Multiphysics by integrating:

Laminar incompressible fluid flow
Electrostatic field simulation
Lagrangian particle tracing
Particle–collector interaction modeling
Parametric numerical simulations

Thousands of numerical simulations were performed over a wide range of operating conditions to understand the governing physics of electrostatic particle collection.

Key Contributions

Developed a validated multiphysics computational framework for charged aerosol transport.
Investigated the role of hydrodynamic drag and electrostatic forces.
Quantified the influence of operating parameters on particle trajectories and collection efficiency.
Generated high-quality simulation datasets used for surrogate machine learning models.
Established the computational foundation for subsequent research on explainable AI, surrogate modeling, and multi-objective optimization.

Technologies Used

COMSOL Multiphysics
Python
Finite Element Analysis
Computational Fluid Dynamics
Scientific Computing

Research Outcomes

This project resulted in multiple peer-reviewed publications and established the numerical foundation for subsequent studies on:

Explainable Artificial Intelligence (SHAP and PDP)
Surrogate Machine Learning Models
Multi-objective Optimization

The developed framework demonstrates how physics-based simulations and machine learning can be integrated to accelerate engineering design while maintaining physical interpretability and predictive reliability.

Journal of Electrostatics (2024)
Influence of Non-Uniform Electric Field Due to Spherical Collector on Particle Dynamics in Quiescent Air