Collaborative Research: Effective Face Masks to Mitigate COVID-19 Transmission: Insights from Multimodal Quantitative Analysis

The years ahead will likely see face masks become a critical and widely used "medical appliance." Understanding the physics that underpins the effectiveness of face masks as a defense against airborne pathogens is therefore, more important than ever. The protection afforded by face masks has emerged as a particularly important issue in the COVID-19 pandemic, but the flow physics of face masks is complex and is not well-studied. The increased pressure inside the mask during expiration pushes the face mask outwards, resulting in increased perimeter leakage. This fluid-structure interaction problem is mediated by the structural design and the permeability of the mask, as well as the fit on the face. Spasmodic events such as coughing and sneezing generate high transient expulsion velocities and significantly diminish the outward protection of face masks. However, in a conceivable future where people will wear face masks while engaged in their daily routines, outward protection during normal activities such as breathing and talking, might be equally important. The objectives of this project are (i) to develop improved computational and experimental tools necessary to characterize the performance of face masks, (ii) to employ these tools to perform a detailed characterization of mask performance under a variety of conditions and (iii) to generate, in a timely manner, data that can be used for improved facemask design and to guide more effective public health policy.

The project will develop a set of innovative, powerful and accurate computational and experimental tools, rooted in flow physics and mechanics that can be used for the quantitative analysis of the protective performance of face masks. Computational tools will couple fluid flows with the motion of elastic structures in complex geometries defined by a wide range of facial geometries. Experimental tools will include simultaneous measurements of mask motion and the aerosol cloud, using Digital Image Correlation and Particle Image Velocimetry, respectively. Visible and X-ray techniques will be used to take measurements outside and inside the mask. The combined results of the simulations and experiments are expected to yield critical insights regarding the features that contribute to the protective performance of masks, and to provide timely guidance for improved mask design and effective public health policies. The project will promote the multidisciplinary education of students in Science and Engineering, and increase public knowledge about the fluid dynamics principles of effective facemasks.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.