A team of researchers from the University of Massachusetts Lowell and California Baptist University has studied how to face masks impact airflow using a computational model. Their findings will likely influence the guidance on mask-wearing and potentially mask design. The computational face mask model was used to predict how masks impact the inhalation and deposition of airborne particles in the upper respiratory tract.
The model was based on a model of a person that was created to be physiologically realistic. The team added a surgical mask with pleats to the model and tracked the particles moving through the mask with numerical methods. The researchers were able to visualize how the particles become deposited in the nose, pharynx, and deep lung. The results of the modeling demonstrated that the mask altered airflow around the face, forcing it to enter the nose and mouth through the entire mask rather than through specific paths.
The air movement into the airways was much slower through the mask than it usually is. This reduction in airflow speed, the researchers found, enhanced the inhalation of aerosols into the nose. The results show that a three-layer surgical mask can achieve filtration efficacy as high as 65% (if new) and as low as 25% (if old). The results also concluded that wearing a mask with a 25% filtration efficacy may be worse than going without.
Ref link: https://aip.scitation.org/doi/10.1063/5.0034580