COVID-19 spread in a classroom equipped with partition – A CFD approach

Abstract

In this study, the motion and distribution of droplets containing coronaviruses emitted by coughing of an infected person in front of a classroom (e.g., a teacher) were investigated using CFD. A 3D turbulence model was used to simulate the airflow in the classroom, and a Lagrangian particle trajectory analysis method was used to track the droplets. The numerical model was validated and was used to study the effects of ventilation airflow speeds of 3, 5, and 7 m/s on the dispersion of droplets of different sizes. In particular, the effect of installing transparent barriers in front of the seats on reducing the average droplet concentration was examined. The results showed that using the seat partitions for individuals can prevent the infection to a certain extent. An increase in the ventilation air velocity increased the droplets’ velocities in the airflow direction, simultaneously reducing the trapping time of the droplets by solid barriers. As expected, in the absence of partitions, the closest seats to the infected person had the highest average droplet concentration (3.80 × 10 −8 kg/m 3 for the case of 3 m/s).

Computational fluid dynamics-based disease transmission modeling of SARS-CoV-2 Intensive Care Unit

“…The CFD analysis model used in this study was the transition SST k - ε model. Mirzaie et al [20] analyzed the effect of installing transparent barriers in front of classroom seats in order to prevent the effect of coughing or sneezing of a COVID-19 infected person standing in the front area of the classroom. It was observed that the barriers were quite effective for preventing the infection.…”

A coupled Computational Fluid Dynamics and Wells-Riley model to predict COVID-19 infection probability for passengers on long-distance trains

“…The COVID-19 pandemic, caused by the transmission of the SARS-CoV-2 virus, has claimed more than 3.88 million lives with more than 178.8 million people known to be infected worldwide ( World Health Organization (WHO) and Coronavirus, 2021 ) (at the time of writing in June 2021). As part of the international effort to combat COVID-19, researchers have employed computational modelling techniques to assess the potential efficacy of non-pharmaceutical mitigation strategies for a range of indoor environments ( Ronchi and Lovreglio, 2020 , Vuorinen et al, 2020 , Malki-Epshtein et al, 2020 , Marcus LJ, et al, Assessment of Risks of SARS-CoV-2 Transmission During Air Travel and Non-Pharmaceutical Interventions to Reduce Risk, Phase One Report: Gate-to-Gate Travel Onboard Aircraft., 2020 , Buonanno et al, 2020 , Rencken et al, 2021 , Zhang et al, 2021 , Li et al, 2021 , Mirzaie et al, 2021 ). An issue of societal concern is the possibility of contracting COVID-19 while confined to the small volume of the passenger compartment of a train carriage (saloon), where passengers are seated in close proximity.…”

“…The Lagrangian approach has also been applied to applications concerning infectious disease transmission ( Vuorinen et al, 2020 , Mirzaie et al, 2021 , Wang et al, 2014 , Peng et al, 2020 , Yan et al, 2017 , Zhang and Li, 2012 , Yang et al, 2018 ). As the Lagrangian approach treats the discrete respiratory aerosol particles as a collection of droplets that are individually tracked through space, it is necessary to specify the initial droplet size distribution, composition and thermodynamic properties – which are not necessarily known.…”

“…Furthermore, given that the discrete phase is directly modelled, with each particle or groups of particles represented, the Lagrangian approach is potentially more computationally demanding than the Eulerian approach. Previous CFD studies of infectious disease transmission utilising the Lagrangian approach have primarily focused on predicting the distribution of respiratory droplets produced by an index patient ( Mirzaie et al, 2021 , Wang et al, 2014 , Peng et al, 2020 , Zhang and Li, 2012 , Yang et al, 2018 ). In addition, some studies have also attempted to determine infection risk based on the predicted droplet distributions ( Vuorinen et al, 2020 , Peng et al, 2020 , Yan et al, 2017 ).…”

Analysis of Infection Transmission Routes through Exhaled Breath and Cough Particle Dispersion in a General Hospital

“…Transient simulations have been conducted to simulate cough jets because the cough flow rate is time-dependent [ 13 , 14 , 15 ]. Mirzaie et al [ 16 ] conducted a numerical simulation of cough droplet dispersion for an infected person in front of a classroom. They found that droplet concentration was lower with higher ventilation rate and the partitions installed in front of the seats.…”