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Register a new userNecessity of ventilation for mitigating virus transmission quantified simply
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To mitigate the SARS-CoV-2 pandemic, officials have employed social distancing and stay-at-home measures, with increased attention to room ventilation emerging only more recently. Effective distancing practices for open spaces can be ineffective for poorly ventilated spaces, both of which are commonly filled with turbulent air. This is typical for indoor spaces that use mixing ventilation. While turbulence initially reduces the risk of infection near a virion-source, it eventually increases the exposure risk for all occupants in a space without ventilation. To complement detailed models aimed at precision, minimalist frameworks are useful to facilitate order of magnitude estimates for how much ventilation provides safety, particularly when circumstances require practical decisions with limited options. Applying basic principles of transport and diffusion, we estimate the time-scale for virions injected into a room of turbulent air to infect an occupant, distinguishing cases of low vs. high initial virion mass loads and virion-destroying vs. virion-reflecting walls. We consider the effect of an open window as a proxy for ventilation. When the airflow is dominated by isotropic turbulence, the minimum area needed to ensure safety depends only on the ratio of total viral load to threshold load for infection. The minimalist estimates here convey simply that the equivalent of ventilation by modest sized open window in classrooms and workplaces significantly improves safety.
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Indoor ventilation is essential for a healthy and comfortable living environment. A key issue is to discharge anthropogenic air contamination such as CO2 gas or, more seriously, airborne respiratory droplets. Here, by employing direct numerical simulations, we study the mechanical displacement ventilation with the realistic range of air changes per hour (ACH) from 1 to 10. For this ventilation scheme, a cool lower zone is established beneath the warm upper zone with the interface height h depending on ACH. For weak ventilation, we find the scalings relation of the interface height h ~ ACH^{3/5}, as suggested by Hunt & Linden (Build. Environ., vol. 34, 1999, pp. 707-720). Also, the CO2 concentration decreases with ACH within this regime. However, for too strong ventilation, the interface height h becomes insensitive to ACH, and the CO2 concentration remains unchanged. Our results are in contrast to the general belief that stronger flow is more helpful to remove contaminants. We work out the physical mechanism governing the transition between the low ACH and the high ACH regimes. It is determined by the relative strength of the kinetic energy from the inflow, potential energy from the stably-stratified layers, and energy loss due to drag. Our findings provide a physics-based guideline to optimize displacement ventilation.
The SARS-CoV-2 virus is primarily transmitted through virus-laden fluid particles ejected from the mouth of infected people. Face covers can mitigate the risk of virus transmission but their outward effectiveness is not fully ascertained. Objective: by using a background oriented schlieren technique, we aim to investigate the air flow ejected by a person while quietly and heavily breathing, while coughing, and with different face covers. Results: we found that all face covers without an outlet valve reduce the front flow through by at least 63% and perhaps as high as 86% if the unfiltered cough jet distance was resolved to the anticipated maximum distance of 2-3 m. However, surgical and handmade masks, and face shields, generate significant leakage jets that may present major hazards. Conclusions: the effectiveness of the masks should mostly be considered based on the generation of secondary jets rather than on the ability to mitigate the front throughflow.
The spread of COVID19 through droplets ejected by infected individuals during sneezing and coughing has been considered as a matter of key concern. Therefore, a quantitative understanding of the propagation of droplets containing virus assumes immense importance. Here we investigate the evolution of droplets in space and time under varying external conditions of temperature, humidity and wind flow by using laws of statistical and fluid mechanics. The effects of drag, diffusion and the gravity on droplets of different sizes and ejection velocities have been considered during their motion in the air. In still air we found that bigger droplets traverse larger distance but the smaller droplets remain suspended in the air for longer time. So, in still air the horizontal distance that a healthy individual should maintain from an infected one is determined by the bigger droplets but the time interval to be maintained is determined by the smaller droplets. We show that in places with wind flow the lighter droplets travel larger distance and remain suspended in the air for longer time. Therefore, we conclude that both temporal and the geometric distance that a healthy individual should maintain from an infected one is determined by the smaller droplets under flowing air which makes the use of mask mandatory to prevent the virus. The maintenance of only stationary separation between healthy and infected individuals is not substantiated. The quantitative results obtained here will be useful to devise strategies for preventing the spread of other types of droplets also containing microorganisms.
Understanding the mechanisms of complex systems is very important. Networked dynamical system, that understanding a system as a group of nodes interacting on a given network according to certain dynamic rules, is a powerful tool for modelling complex systems. However, finding such models according to the time series of behaviors is hard. Conventional methods can work well only on small networks and some types of dynamics. Based on a Bernoulli network generator and a Markov dynamics learner, this paper proposes a unified framework for Automated Interaction network and Dynamics Discovery (AIDD) on various network structures and different types of dynamics. The experiments show that AIDD can be applied on large systems with thousands of nodes. AIDD can not only infer the unknown network structure and states for hidden nodes but also can reconstruct the real gene regulatory network based on the noisy, incomplete, and being disturbed data which is closed to real situations. We further propose a new method to test data-driven models by experiments of control. We optimize a controller on the learned model, and then apply it on both the learned and the ground truth models. The results show that both of them behave similarly under the same control law, which means AIDD models have learned the real network dynamics correctly.
In this Letter we report new effects of resonance detuning on various dynamical parameters of a generic 3-wave system. Namely, for suitably chosen values of detuning the variation range of amplitudes can be significantly wider than for exact resonance. Moreover, the range of energy variation is not symmetric with respect to the sign of the detuning. Finally, the period of the energy oscillation exhibits non-monotonic dependency on the magnitude of detuning. These results have important theoretical implications where nonlinear resonance analysis is involved, such as geophysics, plasma physics, fluid dynamics. Numerous practical applications are envisageable e.g. in energy harvesting systems.
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