Filtering efficiency model that includes the statistical randomness of non-woven fiber layers in facemasks

Area of Science:

• Computational fluid dynamics
• Particle transport phenomena
• Materials science

Background:

• Facemasks are crucial for mitigating virus transmission, yet their filtration efficiency requires further investigation.
• Existing models often simplify facemask structures, limiting their real-world applicability.

Purpose of the Study:

• To develop and validate a computational model for predicting N95 facemask filtering efficiency.
• To investigate droplet capture mechanisms influenced by fiber characteristics and airflow dynamics.

Main Methods:

• A 2D computational model simulating three non-woven fiber layers with random fiber configurations.
• Solving Navier-Stokes and continuity equations for droplet-laden airflow (10 nm to 1.0 µm droplets).
• Simulating airflow velocities from 0.1 m/s to 10 m/s, representing breathing, talking, and coughing.

Main Results:

• The model elucidates mechanical and electrostatic droplet capture efficiencies based on diameter and airflow velocity.
• Simulation results show good agreement with analytical models and experimental data from existing literature.
• The model demonstrates realistic filtration behavior compared to homogeneous, monodisperse fiber models.

Conclusions:

• The developed numerical approach provides a more realistic prediction of N95 facemask performance.
• This model can guide the optimization of anti-viral facemask designs for enhanced protection.
• Understanding filtration mechanisms is key to improving respiratory protective equipment.