CFD-based investigation of the effectiveness of face masks – filtration performance and optimization potential
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Abtract
During the COVID-19 pandemic, masks have become an important protective measure for reducing the spread of infectious aerosol particles. In this work, we discuss the scale dependence of filtration of aerosol particles by a mask from the filter media scale over the filter element scale to the room scale and show how to use GeoDict for filter media optimization of a medical mask.
For the scale-dependence study, we start by validating a GeoDict model based on a 3d structure of a medical mask obtained by microCT to measurements of fractional efficiency carried through at an in-house mask test bench and obtain an excellent agreement. In the next step, porosity and permeability of the mask determined by GeoDict are passed over to near field simulations on the filter element scale where the pressure drop determined by simulation is validated using pressure drop measured at the test rig. Finally, the transport of aerosol particles emitted from a person breathing, speaking or coughing is investigated depending on the mask wearing compliance (no mask, sealed mask, naturally fitted mask). Particle fates are found to be highly dependent on mask wearing.
Varying mean fiber diameter, solid volume fraction and thickness of the filter medium, parametric optimization studies based on a digital twin of the mask sample are carried out. We show that face masks can be designed where filtration efficiency, pressure drop and material consumption is improved simultaneously compared to the base case.