Optimization of Ionic Wind Filtration Systems for Atmospheric Particulate Matter Removal: A Hybrid Numerical and Empirical Modeling Approach

This study presents an optimized numerical and empirical modeling framework for ionic wind-driven electrostatic precipitators designed for atmospheric particulate matter (PM) removal. While traditional particle tracing models in long ducts often suffer from transient evaluation errors (the “flight time paradox”), this work introduces a Fate-based Steady-state Evaluation (FSE) method. By coupling Electrostatics, Laminar Flow, and Particle Tracing in a high-fidelity 2D axisymmetric model, we achieved a baseline validation with a Mean Absolute Error (MAE) of 5.3% compared to experimental data (20 kV, 0.5 m/s). Furthermore, a non-linear regression engine based on a physical-exponential decay function was developed to provide real-time performance predictions. The resulting hybrid model demonstrates a high scientific reliability (R2 = 0.98), establishing it as a robust tool for the design and optimization of air purification systems targeting fine atmospheric aerosols (0.1–3.0 μm). In addition, the proposed Fate-based Steady-state Evaluation (FSE) method eliminates transient bias commonly observed in long-duct Lagrangian particle simulations. This methodological improvement enables statistically consistent efficiency estimation for electrohydrodynamic filtration systems and can be applied to a broad class of Computational Fluid Dynamics (CFD)-based particulate capture studies. The developed framework enables rapid design optimization of compact electrohydrodynamic filtration systems and provides a practical alternative to computationally expensive full-scale Computational Fluid Dynamics (CFD) simulations.