ABSTRACT
In this paper a numerical methodology for close proximity exposure (<2m) is applied to the analysis of aerosol airborne dispersion and SARS-CoV-2 potential infection risk during short journeys in passenger cars. It consists of a three-dimensional transient Eulerian-Lagrangian numerical model coupled with a recently proposed SARS-CoV-2 emission approach, using the open-source software OpenFOAM. The numerical tool, validated by Particle Image Velocimetry (PIV), is applied to the simulation of aerosol droplets emitted by a contagious subject in a car cabin during a 30-minute journey and to the integrated risk assessment for SARS-CoV-2 for the other passengers. The effects of different geometrical and thermo-fluid-dynamic influence parameters are investigated, showing that both the position of the infected subject and the ventilation system design affect the amount of virus inhaled and the highest-risk position inside the passenger compartment. Calculated infection risk, for susceptible passengers in the car, can reach values up to 59%. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.
ABSTRACT
The emergence of viral variants has driven a continuous pandemic with a higher possibility of airborne transmission and a larger scale of infective cases, posing greater demands on indoor risk control. However, the role of room-level air recirculation systems (RRSs) in infection control remains unclear due to insufficient detailed research. There are also fewer analyses of the filtering rating of recirculation filters from the perspective of multi-scale particle size. Thus, a simulation procedure to assess the performance of RRSs on infection control that accounts the transient recirculation of real virus-laden particles in multi-scale sizes was proposed, and focusing on recirculation filter strategies to balance the risk limitation and energy cost. A poorly ventilated winter classroom was selected as a typical environment equipped with RRSs to operate this procedure. Different RRS strategies (i.e., wall-mounted air conditioners (WMAC), floor-standing air conditioners (FSAC) and 4-way cassette air conditioners (WCAC)) were compared. The results show the important contribution of recirculated particles to accumulating the overall infection risk of susceptible occupants towards a high basic reproduction number (Ro > 1). Then, there is a strong correlation of the spatial distributions between high-risk zones and large vortexes at the breathing height of susceptible occupants. Considering rating suitability and filtration effectiveness, the optimization of recirculation filters on energy and cost can be suggested with comparable benefits of infection control. © 2023 Elsevier B.V.
ABSTRACT
The education sector has suffered a catastrophic setback due to the ongoing COVID pandemic, with classrooms being closed indefinitely. The current study aims to solve the existing dilemma by examining COVID transmission inside a classroom and providing long-term sustainable solutions. In this work, a standard 5 × 3 × 5 m3 classroom is considered where 24 students are seated, accompanied by a teacher. A computational fluid dynamics simulation based on OpenFOAM is performed using a Eulerian-Lagrangian framework. Based on the stochastic dose-response framework, we have evaluated the infection risk in the classroom for two distinct cases: (i) certain students are infected and (ii) the teacher is infected. If the teacher is infected, the probability of infection could reach 100% for certain students. When certain students are infected, the maximum infection risk for a susceptible person reaches 30%. The commonly used cloth mask proves to be ineffective in providing protection against infection transmission, reducing the maximum infection probability by approximately 26% only. Another commonly used solution in the form of shields installed on desks has also failed to provide adequate protection against infection, reducing the infection risk only by 50%. Furthermore, the shields serve as a source of fomite mode of infection. Screens suspended from the ceiling, which entrap droplets, have been proposed as a novel solution that reduces the infection risk by 90% and 95% compared to the no screen scenario besides being completely devoid of fomite infection mode. The manifestation of infection risk in the domain was investigated, and it was found out that in the case of screens the maximum infection risk reached the value of only 0.2 (20% infection probability) in 1325 s. © 2023 Author(s).
ABSTRACT
A computational fluid dynamics (CFD) simulation was performed to model and study the transmission risk associated with cough-related SARS-CoV-2 droplets in a real-world high-speed train (HST). In this study, the evaporating of the droplets was considered. Simulation data were post-processed to assess the fraction of the particles deposited on each passenger's face and body, suspended in air, and escaped from exhausts. Firstly, the effects of temperature, relative humidity, ventilation rate, injection source, exhausts' location and capacity, and adding the physical barriers on evaporation and transport of respiratory droplets are investigated in long distance HST. The results demonstrate that overall, 6–43% of the particles were suspended in the cabin after 2.7 min, depending on conditions, and 3–58% of the particles were removed from the cabin in the same duration. Use of physical barriers and high ventilation rate is therefore recommended for both personal and social protection. We found more exhaust capacity and medium relative humidity to be effective in reducing the particles' transmission potential across all studied scenarios. The results indicate that reducing ventilation rate and exhaust capacity, increased aerosols shelf time and dispersion throughout the cabin.
ABSTRACT
The COVID-19 pandemic has shown that airborne pathogens and viruses have a detrimental impact on the health and well-being of an individual in an indoor space. Respiratory particles are released as droplets of varying velocities and diameters, where smaller droplets (aerosols) linger in air for prolonged periods, increasing the infection risk of individuals in an enclosed space. The pandemic has raised concerns regarding the safety of musicians due to respiratory particles released through woodwind and brass instruments. A collaboration with the Buffalo Philharmonic Orchestra was pursued to assess the risk of infection and develop strategies to mitigate the spread of respiratory particles using computational fluid dynamics. A coupled Eulerian-Lagrangian modeling approach was employed to examine the airflow patterns and airborne particle pathogen transport induced by the musicians in the music hall. The investigation considered three brass instruments (trumpet, tuba, trombone), without and with a bell covering. It was observed that the dispersion of particles for each instrument depended on the bell design and orientation of the instrument. For example, the trumpet produced a higher concentration of respiratory particles compared to a tuba, which has its tubing wrapped. Additionally, the effect of using bell covers (cloth covering on the opening of the brass instruments) showed that the covers reduced the number of pathogens escaping the instruments by capturing large respiratory particles and reducing the escaping velocity of small particles. Reduced particle velocities at the instrument opening meant that the particles traveled shorter distances, which helped mitigate the spread of virus in the music hall. Moreover, the efficacy of using Plexiglas partitions on the sides and in front of the musicians limited the transmission of pathogens from one musician to another. Overall, the findings of this study helped strategize the location of musicians based on the type of instruments being played and the operating conditions in the music hall to decrease the airborne transmission of the novel Coronavirus. © 2021 by ASME.
ABSTRACT
Airborne pathogen respiratory droplets are the primary route of COVID19 transmission, which are released from infected people. The strength and amplitude of a release mechanism strongly depend on the source mode, including respiration, speech, sneeze, and cough. This study aims to develop a simplified model for evaluation of spreading range (length) in sneeze and cough modes using the results of Eulerian-Lagrangian CFD model. The Eulerian computational framework is first validated with experimental data, and then a high-fidelity Lagrangian CFD model is employed to monitor various scale particles' trajectory, evaporation, and lingering persistency. A series of Eulerian-Lagrangian CFD simulations is conducted to generate a database of bioaerosol release spectrum for the release modes in various thermal conditions of an enclosed space. Eventually, a correlation fitted over the data to offer a simplified airborne pathogen spread model. The simplified model can be applied as a source model for design and decision-making about ventilation systems, occupancy thresholds, and disease transmission risks in enclosed spaces. © 2021 Institute of Physics Publishing. All rights reserved.
ABSTRACT
Airborne transmission is an important route of spread of viral diseases (e.g., COVID-19) inside the confined spaces. In this respect, computational fluid dynamics (CFD) emerged as a reliable and fast tool to understand the complex flow patterns in such spaces. Most of the recent studies, nonetheless, focused on the spatial distribution of airborne pathogens to identify the infection probability without considering the exposure time. This research proposes a framework to evaluate the infection probability related to both spatial and temporal parameters. A validated Eulerian-Lagrangian CFD model of exhaled droplets is first developed and then evaluated with an office case study impacted by different ventilation strategies (i.e., cross- (CV), single- (SV), mechanical- (MV) and no-ventilation (NV)). CFD results were analyzed in a bespoke code to calculate the tempo-spatial distribution of accumulated airborne pathogens. Furthermore, two indices of local and general infection risks were used to evaluate the infection probability of the ventilation scenarios. The results suggest that SV has the highest infection probability while SV and NO result in higher dispersions of airborne pathogens inside the room. Eventually, the time history of indices reveals that the efficiency of CV and MV can be poor in certain regions of the room.
ABSTRACT
COVID19 pathogens are primarily transmitted via airborne respiratory droplets expelled from infected bio-sources. However, there is a lack of simplified accurate source models that can represent the airborne release to be utilized in the safe-social distancing measures and ventilation design of buildings. Although computational fluid dynamics (CFD) can provide accurate models of airborne disease transmissions, they are computationally expensive. Thus, this study proposes an innovative framework that benefits from a series of relatively accurate CFD simulations to first generate a dataset of respiratory events and then to develop a simplified source model. The dataset has been generated based on key clinical parameters (i.e., the velocity of droplet release) and environmental factors (i.e., room temperature and relative humidity) in the droplet release modes. An Eulerian CFD model is first validated against experimental data and then interlinked with a Lagrangian CFD model to simulate trajectory and evaporation of numerous droplets in various sizes (0.1 µm-700 µm). A risk assessment model previously developed by the authors is then applied to the simulation cases to identify the horizontal and vertical spread lengths (risk cloud) of viruses in each case within an exposure time. Eventually, an artificial neural network-based model is fitted to the spread lengths to develop the simplified predictive source model. The results identify three main regimes of risk clouds, which can be fairly predicted by the ANN model.
ABSTRACT
Nowadays the use of public transportation (PT) has been identified as high risk as due to the transfer of particles carrying the coronavirus from an infected passenger to others. This study puts forward a new computational framework for predicting the spread of droplets produced while the infected passenger talking inside the cabin of a train during various scenarios, including the changes in the outflows' location and the infected passenger's position. CFD was used to conduct the study, using the Euler-Lagrange approach to capture the transmission of particles, and Reynolds-averaged Navier-Stokes equations (RANS) to compute the airflow field. The results revealed that opening the window reduces the duration of particles inside the domain. So that when the window is open, the particle's shelf time can decrease to 25 percent comparing with closed mode. It was found that the passenger sitting next to the infected passenger encountered the highest infection risk. The conclusions made in this work show that the most desirable situation is obtained when the infected passenger is sitting next to the exits, whether the window is closed or open. The results of this paper offer comprehensive insights into how to keep indoor environments safe against infection aerosols.
ABSTRACT
The effect of indoor airflow has been confirmed on the diffusion and transmission of droplets generated when talking or sneezing by a person with a viral respiratory infection such as COVID-19. The present study to investigate the effect of airflow in an indoor environment (a classroom) on the distribution and transmission of droplets emitted from speaking and cough by an infected person. A numerical analysis to investigate the persistence and deposition of particles on the surfaces of desks and the faces of residents (teacher and students) under various scenarios, including the opening of windows. This study puts forward two types of conditions while the teacher is speaking and the cough of some students for the distribution of pathogenic particles. Computational Fluid Dynamics used to conduct the study, using the Euler-Lagrange approach to capture the transport of the particles, and the RANS equations to compute the airflow field in the classroom. The results indicate the significant effect of air conditioning and open window close to the infected person in reducing environmental pathogens. Moreover, the concentrations of virus particles increase greatly near the output; hence, the presence of people in these areas increases the risk of contracting the disease. Furthermore, when all the windows are closed, due to the low output capacity, the particles spread in all areas of the domain and increase the risk of infection. Therefore, it is recommended that the window be open in indoors environment especially the window next to the speaker.