Face covering, commonly known as a facemask, is considered to be one of the most effective pieces of personal protective equipment to reduce transmissions of pathogens through respiratory droplets-both large droplets and liquid aerosol particles. Facemasks inhibit the expulsion of such respiratory droplets from the user and protect the user from inhaling pathogen-laden potentially harmful droplets or their dried nuclei. While the efficacies of various dry face masks have been explored in the recent past, a comprehensive investigation of a wet mask is lacking. Yet users wear masks for an extended period and, consequently, due to the deposition of respiratory droplets released through multiple respiratory events, the mask matrix becomes wet. We, herein, present an experimental study on the dynamics of sequential impacts of droplets on masks to understand how wetness affects possible penetration and secondary atomization of the impacted droplet. Two different types of masks, hydrophobic and hydrophilic, were used in this study to evaluate the underlying physical mechanisms that control the penetration in each of them.
We investigate the dynamic characteristics corresponding to the structural fluctuations of a cantilever suspended in a turbulent flow. To investigate the intricate dynamics of the flow–structure interaction, first, we explore the ability of network analysis to identify the different dynamic states and probe the viability of using quantifiers of network topology as precursors for the onset of limit-cycle oscillations. By increasing the Reynolds number, we observe that the structural oscillations, measured using a strain gauge, transition from low-amplitude chaotic oscillations to large-amplitude periodic oscillations associated with limit-cycle oscillations. We characterize the dynamic states of the system by constructing the weighted correlation network from the time series of strain and identifying the network properties that have the potential to be used as precursors for the onset of limit-cycle oscillations. Furthermore, we use Pearson correlation to illustrate the evolution of mutual statistical influence between the structural oscillations and the flowfield. We use this information and the Granger causality to identify the causal dependence between the structural oscillations and velocity fluctuations. By identifying the causal variable during each regime, we illustrate the directional dependence through a cause–effect relationship in this flow–structure interaction as it transitions to limit-cycle oscillations.
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