Can Airflow Manipulation Disrupt the Transmission of COVID-19 Variants and Highly Infectious Droplets?

doi:10.21203/rs.3.rs-4018265/v1

Download PDF

Research Article

Can Airflow Manipulation Disrupt the Transmission of COVID-19 Variants and Highly Infectious Droplets?

https://doi.org/10.21203/rs.3.rs-4018265/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

In the ongoing battle against new variants of COVID-19 and airborne-transmitted diseases, the focus on indoor air quality, particularly in enclosed spaces, has intensified. This study utilizes computational fluid dynamics (CFD) modelling to investigate how different air distribution setups can impact the spread of airborne COVID-19 particles. Air distribution systems are at the forefront of this research, specifically examining supply and exhaust diffuser placements and their effects on droplet dispersion dynamics. Results reveal a promising reduction (10–21%) in residual droplet mass over a 10-second period when exhaust diffusers are strategically located above cough sources. This underscores the pivotal role of ventilation design in curbing airborne transmission. Furthermore, the analysis sheds light on variations (2.7–8.9%) in droplet Sauter mean diameter across different configurations, underscoring the significance of airflow patterns in dictating droplet size distribution and infection control efficacy. The study also emphasizes the importance of maintaining social distancing measures, showcasing a substantial decrease (82–89%) in viral concentration at a 2-meter distance, despite ventilation imperfections. In summary, this study highlights the critical role of ventilation design in combating airborne COVID-19 transmission within office environments. These findings offer valuable insights into optimizing airflow patterns, enhancing overall safety measures, and informing effective strategies to tackle the pandemic.

COVID-19

Face masks

CFD

Airborne Transmission

Cough droplet lifetime

Exposure risks

Indoor Air Quality

Ventilated rooms foundation

In December 2019, a new coronavirus outbreak emerged in Wuhan, China, swiftly spreading across the nation and beyond. By February 12, 2020, the World Health Organization officially labelled the illness caused by this virus as coronavirus disease 2019 (COVID-19). The fact that coronavirus disease 2019 (COVID-19) cases are still increasing internationally is a worrying development[1] [2]. To lessen, manage, and halt the spread of COVID-19, a number of strategies have been implemented[3], [4]. According to Christie et al. (vaccination is thought to be the most efficient way to contain the outbreak [5]. Indoor air quality plays a vital role in the comfort, well-being, and performance of occupants [6]. Indoor pollution can cause long-term and immediate health effects, responsible for 4.1% of global deaths. Ensuring optimal indoor air quality holds significant importance within operating rooms, given the potential for surgical site infections resulting from air contamination. An effective ventilation strategy is imperative for mitigating indoor pollutants and upholding sterile conditions during surgical procedures [7], [8]. Accordingly, a study employing computational fluid dynamics aimed to assess airflow patterns and the dispersion of airborne contaminants indoors [9]. The findings indicated that using warming blankets effectively decreases the concentration of bacteria-carrying particles near the wound, mostly by facilitating warm upward air currents. A unique surgical lamp mounted on a fan also effectively decreased contamination to an acceptable range for surgeries susceptible to infections. A protective curtain significantly reduced the exposure level of the medical team to a patient with infectious respiratory disease. Diffuse ceiling ventilation systems resulted in the highest cooling capacity and thermal comfort in clinic waiting rooms. The study included visualizations of the airflow field and airborne particles in operating rooms using virtual reality techniques. These findings contribute to reducing infection risk after surgery, providing a deep understanding of airflow patterns and contamination distribution [9].

Extending the application of CFD modeling to COVID-19 research has provided crucial insights into virus transmission and prevention strategies [10], [11]. Despite outdoor social distancing measures, studies have revealed the potential for wind to carry saliva droplets up to 6 meters, emphasizing the importance of indoor ventilation in reducing the concentration of infected air. Combining aerosol disinfectants with air-conditioning systems has shown promise in facilitating wider dispersion of disinfectants, effectively reducing the number of infected particles. The modeling has also shown that sneezing generates more concentrated particle clouds compared to coughing. Optimizing ventilation efficiency involves increasing air change rates, utilizing outdoor air filters, minimizing air recirculation, and ensuring effective air distribution. Furthermore, the use of facial masks has been found to significantly reduce the distance traveled by respiratory particles, capturing a high percentage of them. UV-C light has been shown to enhance room disinfection rates by 50–85%. Finally, positioning virus biosensors properly can reduce the detection time of the virus. These findings underscore the importance of implementing a combination of measures such as ventilation, facial masks, disinfection methods, and strategic placement of biosensors to effectively prevent the transmission of COVID-19 in various settings.

Exploring the intersection of CFD with healthcare infrastructure, this study delves into the application of CFD techniques to model and optimize the internal environment of hospital air conditioning systems. This study investigates the use of CFD techniques to model the internal environment of hospital air conditioning systems, with the aim of improving air quality and reducing the risk of patient infection. Controlling air quality is crucial in venues such as operating theatres, where patients undergo complex surgeries, and in ICU/transplant/oncology departments to minimize the risk of infection. CFD analysis facilitates the examination of airflow patterns and temperature dispersion, enabling air circulation enhancement to attain increased sterility in places prone to risks. The study shows that CFD analysis can be used to model operating rooms and identify flaws in air distribution, allowing for adjustments to achieve optimum flows and temperature. The use of graphic displays shows velocity distributions and temperature distribution, leading to specific placement of filters, diffusers, and identification of the effects of lights, staff, and pendants. Overall, CFD analysis can be a valuable tool in improving air quality and reducing the risk of infection in hospital environments [12].

In another realm of research, Computational Fluid Dynamics (CFD) was employed to delve into strategies aimed at enhancing human thermal comfort, indoor air quality, and the energy efficiency of building Heating, Ventilation, and Air Conditioning (HVAC) systems. The study employed a case study methodology to assess the behaviour of a practical thermal zone and investigate the feasibility of incorporating an adaptive diffuser into the HVAC system. The study also aimed to enhance indoor air quality by examining the spread of airborne infectious illnesses, such as the Coronavirus. The study examined ways to minimize the risk of infection by optimizing HVAC operation, limiting the concentration of expelled pathogens at the source, and using personal protection equipment. The investigation additionally examined how various face masks and barriers affected airflow and the transmission of airborne pathogens in a concert venue where brass instruments are played. To improve interior air quality and prevent disease spread, the study found that bell coverings with Plexiglas separators and face protection could be useful [13].

When a person speaks, breathes, coughs, or sneezes, droplets are released by the respiratory system [[14], [15]]. These droplets can transport infections and cause infectious diseases in people because of their wide size distribution. Airborne contaminated particles can spread the contaminated agents of infectious diseases such measles, SARS-CoV, MERS-CoV (Middle-East Respiratory Syndrome coronavirus), and tuberculosis [16], [17], [18].

In addition to direct contact with an infected individual, these extremely infectious respiratory viruses can spread by both short- and long-range pathways [19], [20].

Since the start of the COVID-19 outbreak, numerous researchers have worked to identify the ways in which this virus spreads. The ventilation duct apertures in a COVID-19 ward at the hospitals were examined by Nissen et al. [21]. They reported the SARS-CoV-2 virus as an airborne transmission virus after finding the virus in the central ventilation duct. Zhang et al. [22] discovered that airflow was the primary method of SARS-CoV-2 transmission. According to their findings, surgical masks should be worn in public areas to stop the infection from spreading. Zuo [23] assessed the SARS-CoV-2 virus's interaction with human respiratory systems using molecular dynamic simulations. Their research recommended taking early action to stop the SARS-CoV-2 from spreading by air.

To forecast how SARS-CoV-2 will travel through the air, more research was done on the virus' aerosol dissemination. In a university café, Zhao et al. [24] investigated the spread of COVID-19 through coughing using a variety of numerical techniques. Their findings illustrated the virus's potential propagation route from a single person to the entire simulated ecosystem. According to Shah et al.'s experimental study [25], almost half of the aerosols emitted managed to get past the surgical masks. According to the same study, utilizing face masks was less effective than installing a suitable ventilation system in containing the spread of airborne infectious diseases like COVID-19.

To provide clean air in indoor spaces [26]. Therefore, one essential strategy for giving people a clean environment is to use ventilation systems [27].

In analyzing the effectiveness of ventilation systems during coughing episodes in healthcare settings, the study reveals significant limitations in the ability of the evaluated configuration to contain airborne particles. Notably, a significant portion of particles encountered by healthcare workers remained unaffected by the alternative ventilation setup designed for airborne infection isolation. The exhaust system's impact on eliminating cough aerosols was minimal, except in proximity to high-velocity exhaust grills. Furthermore, the study elucidates a concerning pattern where released aerosols disintegrate into smaller particles that persist in the air, re-entering the supply air stream and dispersing throughout the room within 5–20 seconds post-cough. Even after a single cough cycle, these suspended aerosols lingered for more than 21 seconds, emphasizing the critical importance of well-designed ventilation systems to effectively curtail the spread of infectious diseases and safeguard the well-being of healthcare workers in healthcare settings

[28].

Utilizing computational fluid dynamics (CFD) as its methodology, this study delved into examining how coronaviruses dispersed within droplets from a coughing-infected individual within a classroom setting, taking into account different ventilation airflow speeds and the presence of transparent barriers. Notably, the findings demonstrated that the use of seat partitions had a partial preventive effect on the transmission of infections. Higher ventilation air velocities resulted in increased droplet velocities, reducing the trapping time of droplets by solid barriers. In the absence of partitions, seats closest to the infected person exhibited the highest average droplet concentration. A key observation was that heightened ventilation velocity correlated with a reduction in the number of suspended droplets. Furthermore, classrooms without partitions took longer to achieve a negligible droplet concentration compared to those with partitions, and increasing ventilation speed expedited this process. Over time, the average droplet concentration decreased, with the lowest ventilation speed showing the highest concentration. Comparatively, classrooms with partitions consistently exhibited lower average droplet concentrations than those without. Seat 3 demonstrated the highest average droplet concentration in the absence of partitions, while in the presence of partitions, Seat 20 (at 3 m/s) and Seat 2 (at 7 m/s) experienced the highest exposures [29].

Employing computational fluid dynamics (CFD) modeling, this study investigated airborne pathogen concentrations during a COVID-19 outbreak in a Guangzhou restaurant. Simulating seating arrangements, overlap times, and ventilation, the study found that predicted infection patterns aligned with reported rates around the index patient. Altering ventilation conditions showed a 30%, 70%, and 80% reduction in pathogen mass with a 10%, 50%, and 100% fresh-air supply, respectively, over a 73-minute period. Correspondingly, infection likelihood decreased by 10%, 40%, and 50%, highlighting the critical role of ventilation adjustments in reducing indoor infection risks [30].

Building upon prior research, this study introduces a novel framework for evaluating infection probabilities in confined spaces, utilizing Computational Fluid Dynamics (CFD) to simulate airflow dynamics within an office environment under various ventilation strategies. Results underscore the critical influence of ventilation configurations on pathogen dispersion, with single-ventilation (SV) and no-ventilation (NV) scenarios presenting heightened infection probabilities. Employing visualization tools such as 3D scatter plots and dispersion patterns are elucidated, while the introduction of local and global indices allows for quantitative comparisons. This framework offers practical applications in assessing ventilation systems and optimizing occupant layouts, with acknowledgment of inherent limitations and a focus on refining practical solutions for mitigating airborne pathogen transmission in confined spaces

[31].

Leveraging computational fluid dynamics (CFD) simulations, this study rigorously examines the intricate dynamics governing the movement and evaporation of multi-component cough droplets within hospital isolation rooms, particularly emphasizing the nuanced influence of diverse ventilation configurations. Researchers recommended optimal exhaust vent placement to enhance the removal of infectious droplets based on the effects of different air outlet positions on droplet removal efficiency. The findings underscored the significant impact of relative humidity (RH) on droplet evaporation rates. Low RH led to the rapid evaporation of large droplets, transforming them into small aerosols that could linger in the air for extended periods. This phenomenon explained the increased transmission of Covid-19 in low-humidity winter conditions compared to high-humidity summer conditions. The study emphasized the critical role of outlet location in ventilation design. When the exhaust outlet was positioned above the patient's head, a 99% droplet removal efficiency was achieved within 90 seconds. Additionally, the researchers discovered that the droplet evaporation rate is higher in environments with lower ambient humidity. For instance, droplets with an initial size of 50 µm took approximately 1.7 seconds to evaporate at 20°C and 30% RH. At 90% RH, the evaporation time increased 3.2 times, indicating a slower evaporation rate. This implies that droplets containing COVID-19 viruses will evaporate more rapidly in low RH environments, leading to their prolonged presence in the air as stable aerosols, particularly for droplets with an initial size of less than 70 µm. In healthcare facilities environments, there is an elevated risk of COVID-19 transmission through the inhalation of these smaller, evaporated droplets. Designing effective controls to mitigate the spread of infectious diseases, such as COVID-19, within healthcare facilities necessitates a comprehensive understanding of the interplay between droplet size, evaporation rate, and relative humidity [32].

Within the controlled environment of a Boeing 737 model cabin, this investigation meticulously utilizes numerical simulations to scrutinize the intricate transmission dynamics of COVID-19 via cough-induced particles. The findings uncover a distinct disruption in the local airflow field as cough flows swiftly transform into turbulent cough jets, particularly within the initial 1.5 seconds, exerting considerable influence on particle dispersion. Notably, particles discharged by passengers seated at window seats pose the highest exposure risks, dispersing extensively within the cabin. In contrast, particles from aisle-seat passengers present the lowest risk of exposure to nearby individuals, while those from middle-seat occupants tend to remain contained within their immediate vicinity. The pronounced impact of the cough jet is evident in its substantial disruption of the nearby airflow field, notably affecting the air recirculation zone. Moreover, divergent transport patterns are observed for intermediate-sized particles across various seating scenarios, with larger particles demonstrating comparatively faster deposition. As a practical recommendation, individuals identified as potential index patients are advised to wear masks, particularly when seated adjacent to a window. Furthermore, future research endeavors should diligently address acknowledged limitations, including considerations such as the implementation of periodic boundary conditions and the exclusion of droplet evaporation effects [33].

In addition to the comprehensive literature provided, other authors reviewed experimental and numerical studies on human airflows, encompassing sneezing, coughing, and breathing, focusing on COVID-19 transmission dynamics. It explores droplet size impacts, considering viscosity and relative humidity effects. The study confirms facemask effectiveness in lowering COVID-19 transmission, filtering particles under 10 microns. Key findings include smaller cough droplets travelling longer distances, larger droplets having extended lifetimes due to viscoelasticity, and significant droplet lifetime increases at higher relative humidity (up to 150 times at 90% RH). Particle penetration into the environment is linked to mouth opening, with small and medium particles showing similar penetration and large particles dropping quickly at short distances. No substantial difference is found between healthy and sick volunteers. Propagation distance and maximum velocity depend on droplet size and patient gender, with males experiencing higher peak velocities and longer distances. Computational fluid dynamics (CFD) for virus transmission simulation requires precise boundary conditions. Direct numerical simulation (DNS) over Reynolds-averaged Navier–Stokes (RANS) models are favoured. High-resolution large eddy simulation (LES) provides quantitative insights into aerosol cloud evolution in indoor environments. Multilayer cloth facemasks are recommended for enhanced protection [34].

In investigating the transmission dynamics of the COVID-19 virus within confined spaces, particularly elevators, this study underscores the critical role played by respiratory droplets. The distribution of these droplets is tightly connected to elements such as airflow, ambient temperature, and humidity levels. Under two opposing ecological scenarios, the study investigates three different ventilation modes: quiescent, axial exhaust draft, and exhaust fan. The danger assessment, measured by a calculated risk factor using average droplet counts at the passenger's hand-to-head area, shows a significant reduction from 40% in calm situations to 0% when using exhaust fan ventilation in hot, dry conditions. Evaporation dynamics, particularly in cold and humid conditions, contribute to the rapid settling of droplets below the identified risky height zone. The introduction of forced circulation ventilation, characterized by increased air velocity, accelerates droplet evaporation, further reducing the risk. The study identifies a critical radial distance with the highest concentration of virusols, emphasizing their heightened viral loading and potential for increased transmission. Despite the significance of these findings, the study acknowledges the inherent limitations of its scenarios, urging caution and thorough precautionary measures during elevator use, as real-world conditions may vary considerably from those considered in this investigation [35].

In another study, a numerical model incorporating gravitational sedimentation and transport during breathing, sneezing, or coughing to investigate the spread of droplets or particles in a ventilated room. The numerical simulations consider three scenarios: normal breathing, coughing, and sneezing, each with varying rates of particle ejection from the mouth. Utilizing the Navier-Stokes equations for incompressible flows, three-dimensional airflow inside ventilated rooms is comprehensively described. The study explores the impact of ventilation rates on social distancing and reveals that particles can travel up to 5 meters with a reduction in concentration along the airflow direction. Contrary to the World Health Organization's (WHO) recommended two-meter social distance, the findings indicate insufficiency under the studied environmental conditions. Computational Fluid Dynamics (CFD) is employed to analyze particle transport and scattering, considering sizes ranging from 10^(-4) to 10^(-6). The validation of the ventilation model aligns well with experimental data, affirming the efficiency of the modelling mechanism. Notably, the study underscores the limited transport of particles during normal breathing compared to the substantial distances covered during sneezing or coughing. In conclusion, the research advocates for personal hygiene practices, such as mask-wearing and covering the nose and mouth during sneezing or coughing, as crucial measures to mitigate long-distance particle transport and reduce the transmission of infectious diseases [36].

Venturing into the realm of computational exploration, this investigation utilizes a URANS CFD (Unsteady Reynolds-Averaged Navier-Stokes Computational Fluid Dynamics) approach to unravel the complex dynamics of saliva droplet aerosols carrying SARS-CoV-2 within enclosed spaces. The primary transmission mode is the release of saliva droplet aerosols during breathing or coughing. The study employs Lagrangian and Eulerian numerical methodologies to model these respiratory events, validating their influence on aerosol diffusion. The simulations are tailored to a realistic scenario involving a two-person meeting room, incorporating variations in aerosol characteristics and ventilation system configurations. The overarching objective is to furnish a robust tool for scrutinizing the optimal utilization and administration of confined spaces where occupants may release pathogens through respiratory events. The analysis underscores the substantial impact of ventilation systems on the flow dynamics within indoor environments, underscoring the necessity to evaluate occupants' safety conditions concerning indoor air distribution. Although specific numerical data is not expressly provided, the proposed CFD pragmatic approach is affirmed as precise in predicting the evolution of respiratory events, offering valuable insights into secure conditions for occupants, particularly amid the COVID-19 pandemic. Furthermore, the simulations exhibit the capability to foresee interpersonal distances deemed safe in specific scenarios, accounting for the influence of ventilation systems in transporting pathogen particles across extended distances [37].

In yet another in-depth study, researchers have employed transient computational fluid dynamics (CFD) modeling to systematically examine the dispersion of COVID-19-laden droplets within enclosed spaces. This investigation aims to evaluate the effectiveness of different preventive strategies.

The results indicate that upward ventilation is the most effective measure, followed closely by face masks. The combination of face masks and upward ventilation emerges as the soundest solution, demonstrating a remarkable reduction of nearly 99.95% in indoor infectious concentration compared to baseline scenarios without preventive measures. The study delves into the intricate characteristics of droplet dispersion, highlighting the dominance of gravitational settlement for larger droplets in the early stages and diffusion coupled with inertial settling for smaller droplets in later stages. Evaporation is identified as a consistent mechanism across all droplet sizes throughout the duration. When considering single precautionary measures, upward ventilation, followed by face masks and protective screens, is the most effective sequence. The study underscores the pivotal role of indoor upward ventilation patterns in safeguarding against COVID-19, emphasizing a recommended Air Changes per Hour (ACH) value of 10 to significantly lower droplet concentration. Furthermore, the convenience and efficiency of face masks are underscored as a key solution for limiting the spread of COVID-19. This comprehensive analysis provides valuable insights into the dynamics of droplet dispersion, offering crucial guidance for developing effective preventive strategies against indoor transmission of highly contagious diseases [38].

As COVID-19 infected individuals cough, they unwittingly propel coronavirus particles into the air, creating a jet-like flow that contaminates their surroundings with the virus.

This study estimates the air volume, which can help design ventilation in closed spaces and reduce the spread of the disease. Recent experiments show that the velocity in a cough-cloud decays exponentially with distance. The study also analyzes the volume of the cough-cloud in the presence and absence of a face mask. The volume entrained in the cloud varies as V = 0.666 c2d3c, where c is the spread rate and dc is the final distance traveled by the cough-cloud. The cough-cloud is present for 5 s-8 s, then dissipates, regardless of the mask's presence. The cough-cloud eventually reaches room temperature while remaining slightly more moist than the surrounding air. These findings have implications for understanding the spread of coronavirus, which is reportedly airborne [39].

To increase the study's comprehensiveness, researchers assess exposure dangers using aerosol concentration as a parameter. Indoor airflow and droplet transmission are simulated using an Euler-Lagrangian model. While the droplets that people exhale are seen to be separate phases, the air is thought to be a continuous phase. The simulation technique may be loosely separated into two primary parts. The Lagrangian model is used to predict the trajectory of cough droplets after the time-dependent Euler model has solved interior flow fields. Moreover, this simulation anticipates droplet evaporation by considering mass and heat transport between droplets and the surrounding air [40], [41].

The simulation will take place in a workplace located in Alexandria, Egypt with an already tested and published research for airflow in various air ceiling diffusers [42]. The physical model used in this study represents an office space with specific dimensions. The office has a height of 3 meters (y), a width of 5 meters (x), and a length of 5.12 meters (z) (Fig. 1) provides a three-dimensional visual representation of the room being analyzed, illustrating the positions of employee mannequins within the space. The human sitting mannequin is 1.5 m sitting height with a 320 mm² mouth opening, the room setting as illustrated in showing two sitting human bodies. The coughing personnel will be sitting in the room marked as the cough source (Fig. 1). In this model, the square air diffuser was used in simulation (Fig. 1).

The model development and simulation process utilized the ANSYS package (Version 19.1), which is a software tool suitable for both qualitative and quantitative assessment of airflow and heat transfer within enclosed spaces. The existing HVAC system [42] served as the foundation for this model. The three-dimensional (3D) geometry of the model was created using ANSYS-Design Modeler, providing precise control over the placement of diffusers. ANSYS-Meshing proved to be a reliable tool for generating the mesh, ensuring optimal control volumes and enhancements around human mannequins and the ceiling diffusers (Fig. 2). The model incorporated the actual building's geometric proportions, as depicted in Fig. 1. ANSYS-FLUENT was responsible for simulation of the Euler-Lagrangian model as FLUENT offers a large selection of boundary conditions and physics models to accurately depict the processes of heat transfer and flow. In addition to defining velocity inlets, pressure outlets, and wall conditions, users may also describe attributes like fluid viscosity, density, and thermal conductivity.

2.1. Euler model (Air model)

As mentioned earlier the simulation process is divided into two phases. The first phase is the flow of air model. This study exploited the Reynolds average Navier-Stokes (RANS) equations with $k-\epsilon$ turbulence models along with the standard wall function [43]. As the primary objective of this research was to evaluate the effectiveness of various particle models, the specific details of the turbulence model regarding airflow are omitted in this paragraph. Comprehensive understanding of CFD modeling of fluid flow and turbulence are found in [44]. The computational domain grid verification can be found in previous published article by authors [42]. As for the lagrangian model (cough model), the focus is on examining the droplets released during coughing. Data related to cough measurements were adapted from [45], that conducted measurements of the size distribution of cough droplets using a microscope. It was reported that the velocity of the expelled airflow during coughing is approximated by combining multiple sine functions, based on experimental data obtained from a spirometer that utilized a Fleish type pneumotachograph. The maximum velocity of the airflow is recorded as 15.9 m/s.

2.2. Particle transportation and sizing (Cough model)

Droplets released by coughing range in size from 1 to 1000 $\mu m$. We make use of two Rosin-Rammler distributions to fit the droplet diameter distributions of 1 to 50 m and 50 to 1000 $\mu m$, respectively, due to the variation of cough droplets diameter. ${Y}_{d}$ is labelled in the Rosin-Rammler distribution function as the mass fraction of droplets having a diameter larger than $d$. Furthermore, according to Rosin and Rammler (1933), there is an exponential connection between ${Y}_{d}$ and $d$.

$${Y}_{d}={e}^{{-\left(\frac{d}{\stackrel{-}{d}}\right)}^{n}}$$

where $d$ is the mean diameter, and $n$ is the spread parameter.

Solid cone injection method was utilized in front of the mouth weighing 7.7 mg [46], [47]. Injection duration was 0.5 seconds with a total of 35000 parcels. According to the overall mass and Rosin-Rammler distribution, each parcel reflects a certain number of droplets.

The angle between the cough airflow and the horizontal is 27.5°, and the droplet parcel injection velocity is 6.6 m/s [48], [49]. The path that cough droplets take is examined using the discrete phase model (DPM). The non-steady motion of individual droplet packets is seen by the application of Newton's second law and the consideration of the effects of drag force, gravity, and buoyancy [50], [51]. According to Nicas et al., 2005, the concentration of non-volatile components in cough droplets is 1.8%, meaning that the equilibrium diameter of the droplet nucleus is approximately 26% of its initial size.

Using the discrete phase model (DPM), the path of cough droplets was investigated. Using Newton's second law and accounting for the effects of drag force, gravity, and buoyancy, each droplet parcel is observed for unstable motion (Anzai et al., 2022).

$$\frac{d\overrightarrow{{u}_{p}}}{dt}=\frac{\overrightarrow{u}-\overrightarrow{{u}_{p}}}{{\tau }_{r}}+\frac{\overrightarrow{g}({\rho }_{p}-\rho )}{{\rho }_{p}}$$

where $\overrightarrow{{u}_{p}}$ is the droplet velocity, ${\tau }_{r}$is the droplet relaxation time, which depends on the diameter and density of the droplet, $\overrightarrow{g}$ is the gravity acceleration, and $\rho$ is the droplet density.[51]

In addition to, the use of the random walk model to describe turbulence-induced droplet dispersion [53].

$${u}_{i}=\stackrel{-}{{u}_{i}}+\zeta \sqrt{\frac{2k}{3}}$$

where $\zeta$ is a normally distributed random number, and $k$ is the turbulent kinetic energy.

2.3 Numerical framework

Boundary conditions were retrieved from the previously published work by authors [42] to setup simulation in the ANSYS-FLUENT application, the conditioned fresh air was introduced to the supply grilles as 1 m/s inlet velocity and 22 $℃$ to as a low-speed setting for the air conditioning unit. the exhaust had an outflow zone set. The human body's surface temperature, measured at 32 $℃$ [54]. The walls, ground, and ceiling are configured to "trap," which indicates that droplets have been deposited after reaching the surface, for the DPM model's boundary conditions. The air supply opening and exhaust vent's boundary condition has been configured to "escape." The second-order upwind scheme is employed to discretize the equations related to momentum, energy, k, and $\epsilon$. To accurately capture the pressure gradient at the boundary, the "PRESTO!" scheme is used to discretize the pressure term. The algebraic equations are solved using a coupled algorithm. Additionally, droplets are tracked for a duration of 10 seconds. A time step of 0.01 seconds is chosen to accurately capture the airflow of the cough jet. The convergence criterion for the simulation is defined as the energy relative residual reaching 10^− 7 and the relative residuals of other variables reaching 10^− 4. Convergence is typically achieved with 20 iterations for each timestep. The numerical setup will vary in this research for the two arrangements of the air conditioning system with the air flow pattern envisioning its impact on the droplets ejected by human cough and the dispersion of cough particles in office surrounding which in turn increase infection rates. To examine so, the Exhaust diffusers will be replaced by supply diffusers and vice versa as shown in Figure 4, contrasting the effect of the parallel exhaust diffusers in the center of the workplace (Case # 1) to the location of the exhaust diffuser above the staff sitting in the office (Case # 2).

2.4. HVAC and Cough particle validation

The validation process was executed in two consecutive steps. First, the HVAC system was previously validated past research [42] With the concept of comparing measurements taken on site. Second, the validation of the cough particle transportation. The DPM model has received extensive validation, performing well in forecasting droplet mobility and deposition. Nonetheless, the velocity of air greatly affects how droplets travel. Prior to examining the droplet lifespan released during a cough, we confirm the cough airflow phase's validity. Agrawal and Bhardwaj developed a mathematical formulation for predicting the distance traveled by a cough cloud in an enclosed area, building upon experimental findings [55]. Validation scenario took place by tracking the movement distance of the cough cloud front (Fig. 5), using the movment of 1 µm diameter particles to estimate the cough cloud front's movement distance. The particles are expelled in front of the mouth and closely tracked with the airflow during the cough. In Fig. 6, a sample of the cough cloud in the current study showing the dispersion of particles in 1 seoconds imaging the the cloud measurment for the validation puropse in 10 seconds time line. The cloud was measured from the front of the mouth to the last particle in cloud. Validation results showed promising results for the current model when comparing witht the mathematical model (Fig. 7). The computational results showed maximum variation of 11% when comparing with the mathematical model [55]. The disagreement decrease significatly while the time pass 3s.

In comparison of the two cases simulated regarding the supply and exhaust diffusers placement as mentioned before, a more detailed analysis is presented in the upcoming section helping in understanding the cough dynamics and the air flow interaction to identify clearly the risk of exposure between the two cases of diffusers settings.

3.1. Maximum travel distance

Evaluated by FLUENT assessing the track of particles inside the room to predict the maximum travel distance by droplets in the room. In Figure 8, the comparison between both cases had been presented to show the same trend and behavior for particles throughout the time frame simulated. On average, Case # 2 where the exhaust diffuser is above the cough source showed lower travel distance for particles. It had been noticed that in case # 2 the difference reached 10 % on average when compared with case # 1. For further exploration and visualization for the previously mentioned figure a particle track shot were taken from FLUENT in the period of 3 seconds, it is clear that in case # 2 the particle dispersion is lighter and inhibited with the supply diffuser above the cough source, while in Case # 1 particles is dragged more into the middle of the room towards the middle exhaust diffusers. With the aid of Figure 10, showing the velocity vectors influence on cough particles on 2,3 and 3 seconds, it is evident that in Case #1, velocity vectors influence the particles towards the parallel diffusers in the middle of the particle cloud, growing it over a period of three seconds. However, in Case #2, most of the velocity vectors are directed upward (approximately 90 degrees) towards the exhaust diffuser on the left (Figure 10), which prevents the particle cloud from extending and ensures the results seen in Figure 8.

3.2. Remaining droplet mass proportion

Characterizing the viral concentration in the room may be done using the droplet mass that remains inside the room and had not been exhausted. It has been demonstrated by determining the relationship between inject droplet mass and residual droplet mass over time through displaying the proportion of droplets mass remaining in the two-diffuser configuration. In Figure 11, both cases working perfectly in exhausting the droplets mass of the cough injected particles reaching 10% for case # 1 and 21% for case # 2 in 10 seconds simulation period. The power of parallel exhaust system for case # 1 configuration had been noticeable specially after 5 seconds period while the cough injection matured and traveled to nearly half of the room.

3.3. Droplets Sauter mean diameter

A more thorough quantitative analysis of the droplet lifetimes while, diameter distribution of droplets in the room alters continuously because of the effects of droplet evaporation, sedimentation, and adhering on the different surfaces and walls. The transmission behavior of droplets is directly influenced by their diameter. To measure how droplet size changes over time, Sauter mean diameter (SMD), commonly referred to as D32, is used (Figure 12). While analyzing Figure 12, it has been found that the droplet diameter didn’t vary significantly when comparing case # 1 to case # 2 for the first 5 seconds. Although the variance is negligible case # 1 was superior in terms of the droplets Sauter mean diameter with a maximum of 2.7% difference at 4 seconds time. The difference was much noticeable in the last 5 seconds (5 < Time < 10) with an average value of 4 % difference. The maximum difference occurred by the end of the period at 10 seconds time were the difference reached 8.9 %. Confirming the droplets mass remaining proportion findings for the power of the parallel exhaust system.

$${D}_{32}=\frac{{\int }_{{D}_{min}}^{{D}_{max}}{D}^{3}dN}{{\int }_{{D}_{min}}^{{D}_{max}}{D}^{2}dN}$$

where N is the number of droplets having a diameter of D

In pursuing the urge to reduce the risk of exposure and stop the virus's spread, a time-averaged particle concentration was calculated in Z-axis planes away from the cough source to establish the optimal diffuser configuration for workspaces and offices as well as the safest social distance. Particle concentrations in front of the cough source served as the datum, and calculations were based on values from the whole simulation as well as on planes that were consequently half a meter away from the cough source. Upon closer inspection of Figure 12, it can be observed that, in the ½ m plane, Case #2 has a larger concentration than Case #1. This proves that, as previously shown in Figure 8, the exhaust diffuser, when positioned above the cough source, slows particle migration towards the room. From a 1 m to a 2.5 m plane, particle concentrations in both cases exhibited a sharp decline. When compared to case # 1, case # 2 exhibits lesser concertation, with a maximum value of 40% drop; nonetheless, case # 1 operated better as seen in Figure 11, which shows the proportion of mass that remains inside the office. This indicates that while the parallel exhaust system used in Case #1 does a great job of exhausting particles, it does not prevent particles from spreading and moving away from the source of the cough. A more thorough investigation of Figure 13 shows that 2 meters is a safe social distance that should be used in workplaces and offices as it decreases concentration by 82% in case #1 and 89% in case #2.

To sum up, this article presents a thorough examination of the forecast of air currents and droplet dissemination in indoor environments through an Euler-Lagrangian system. The integration of both approaches enabled researchers to precisely mimic the interplay between fluid phases that are continuous versus particles or droplets dispersed. Specifically conducted within a workplace located in Alexandria, Egypt; researchers emphasized their investigation on pattern dispersal linked with cough-based droplets. For the purpose of this research, ANSYS software was adopted to create and simulate models with high precision in diffuser positioning as well as heat transfer and flow processes representation. The airflow model utilized Reynolds average Navier-Stokes equations complemented by turbulence models while coughing analysis involved the discrete phase modeling approach for studying droplets released during coughs. The numerical framework was adapted from previously published work by authors while mounting human mannequins to mimic cough dispersion through human mouth. In this study, the numerical configuration differed with two setups of the air conditioning system, supply diffusers will be swapped with exhaust diffusers. This will compare and contrast the impact of parallel exhaust diffusors situated at center stage versus those above infected individuals who are coughing (i.e., Case #1 vs Case#2). The validation process involved two steps: comparing measurements taken on site and validating the cough particle transportation model which is previously published by authors. The DPM model has been validated for forecasting droplet mobility and deposition. The cough airflow phase's validity was confirmed by tracking the movement distance of the cough cloud front. The current model showed promising results when compared to the mathematical model, with a maximum variation of 11%. The analysis compared two simulated cases of supply and exhaust diffuser placement, focusing on cough dynamics and air flow interaction to identify exposure risk. The study used FLUENT to evaluate the maximum travel distance by droplets in a room. The results showed that in case #2, where the exhaust diffuser is above the cough source, particles travel further, with a 10% difference on average compared to case #1. In case #1, particles are dragged more towards the middle exhaust diffusers, while in case #2, velocity vectors are directed upwards, preventing the particle cloud from extending further. The viral concentration in the room was shown using the remaining droplet mass inside the room, which had not been depleted. The study established the link between inject droplet mass and residual droplet mass over time, with both methods efficiently emptying droplet mass. The parallel exhaust system's power became apparent after 5 seconds, when the cough injection developed and reached nearly half of the room. The study examined also droplet lifetimes and diameter distributions in a room due to evaporation, sedimentation, and adhesion. The Sauter mean diameter (SMD) is used to assess how droplet size varies over time. Case #1 demonstrated a considerable change in droplet diameter, reaching a high of 2.7% at 4 seconds. The difference became more obvious in the last 5 seconds, reaching 8.9% after 10 seconds. The main research aim was to decrease exposure and viral propagation by estimating particle concentrations in Z-axis planes distant from cough sources. The results revealed that a safe social distance of 2 meters is advised for businesses and offices. Case #2 exhibited a higher concentration than Case #1, demonstrating that the exhaust diffuser reduces particle migration. However, Case #2 showed less concertation than Case #1, indicating that the parallel exhaust system did not prevent particles from spreading. Simulation results showed that effective air distribution and infection control approaches for enclosed environments need consideration of fluid phases and dispersed particles. This study helps to inform public health policy and safe indoor space recommendations.

Acronyms:

CFD - Computational Fluid Dynamics

COVID-19 - Coronavirus Disease 2019

HVAC - Heating, Ventilation, and Air Conditioning

MERS-CoV - Middle-East Respiratory Syndrome Coronavirus

SARS-CoV - Severe Acute Respiratory Syndrome Coronavirus

SARS-CoV-2 - Severe Acute Respiratory Syndrome Coronavirus 2

UV-C - Ultraviolet-C

RH – Relative humidity

SMD - Sauter mean diameter.

Symbols:

µ – Micro

X – Length

Y – Width

Z – Height

$k$ – Turbulent kinetic energy

$\epsilon$ - Rate of dissipation of turbulent kinetic energy

$u$ – Velocity

${\tau }_{r}$ – Droplet relaxation factor

$\overrightarrow{g}$ – Gravitational acceleration

$\rho -$ Droplet density

$\zeta$ $-$ Normally distributed random number

Abbreviations:

°C - Degrees Celsius

D – Diameter

m - Meters

N - Number of droplets

s – Seconds

X – Length

Y – Width

Z – Height

Funding

The authors declare that no funding was received for this paper.

Competing Declaration

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethics approval and consent to participate

Not Applicable

Consent for Publication

Not Applicable

Author Contributions Statement

All authors (Mina A. Saad, Amr Ali Hassan, Ahmed A. Hanafy, Mahmoud H. Salem, Micheal A. William) have contributed equally and collaboratively to this work, with each member actively engaging in the research, writing, and revision processes to ensure a comprehensive and balanced representation of their collective efforts.

Acknowledgements

Not Applicable

Availability of data and materials

Not Applicable

Z. Y. Zu et al., “Coronavirus Disease 2019 (COVID-19): A Perspective from China,” Radiology, vol. 296, no. 2, pp. E15–E25, Aug. 2020, doi: 10.1148/radiol.2020200490.
M. A. William, M. J. Suárez-López, S. Soutullo, and A. A. Hanafy, “Evaluating heating, ventilation, and air-conditioning systems toward minimizing the airborne transmission risk of Mucormycosis and COVID-19 infections in built environment,” Case Studies in Thermal Engineering, vol. 28, p. 101567, Dec. 2021, doi: 10.1016/j.csite.2021.101567.
T. A. Hassan, S. Hollander, L. van Lent, M. Schwedeler, and A. Tahoun, “Firm-Level Exposure to Epidemic Diseases: COVID-19, SARS, and H1N1,” Cambridge, MA, Apr. 2020. doi: 10.3386/w26971.
K. Dhama et al., “COVID-19, an emerging coronavirus infection: advances and prospects in designing and developing vaccines, immunotherapeutics, and therapeutics,” Hum Vaccin Immunother, vol. 16, no. 6, pp. 1232–1238, Jun. 2020, doi: 10.1080/21645515.2020.1735227.
A. Christie, J. T. Brooks, L. A. Hicks, E. K. Sauber-Schatz, J. S. Yoder, and M. A. Honein, “Guidance for Implementing COVID-19 Prevention Strategies in the Context of Varying Community Transmission Levels and Vaccination Coverage,” MMWR Morb Mortal Wkly Rep, vol. 70, no. 30, pp. 1044–1047, Jul. 2021, doi: 10.15585/mmwr.mm7030e2.
M. A. William, A. M. El-Haridi, A. A. Hanafy, A. El-Hamid, and A. El-Sayed, “Assessing the Energy Efficiency improvement for hospitals in Egypt using building simulation modeling,” vol. 42, no. 1, pp. 21–34, 2019.
M. A. William, A. M. Elharidi, A. A. Hanafy, A. Attia, and M. Elhelw, “Energy-efficient retrofitting strategies for healthcare facilities in hot-humid climate: Parametric and economical analysis,” Alexandria Engineering Journal, vol. 59, no. 6, pp. 4549–4562, Dec. 2020, doi: 10.1016/j.aej.2020.08.011.
M. William, A. El-Haridi, A. Hanafy, and A. El-Sayed, “Assessing the Energy Efficiency and Environmental impact of an Egyptian Hospital Building,” IOP Conf Ser Earth Environ Sci, vol. 397, no. 1, p. 012006, Nov. 2019, doi: 10.1088/1755-1315/397/1/012006.
P. Sadeghian, “Computational fluid dynamics application in indoor air quality and health ,” Doctoral dissertation, KTH Royal Institute of Technology, Stockholm, 2022.
M. Saad, M. A. William, A. A. Hassan, and A. A. Hanafy, “Influence of air ceiling diffusers in enclosed spaces: An experimental and numerical investigation,” Energy Reports, vol. 9, pp. 59–71, Sep. 2023, doi: 10.1016/j.egyr.2023.05.253.
M. A. William, M. J. Suárez-López, S. Soutullo, M. M. Fouad, A. A. Hanafy, and W. M. El-Maghlany, “Multi-objective integrated BES-CFD co-simulation approach towards pandemic proof buildings,” Energy Reports, vol. 8, pp. 137–152, Nov. 2022, doi: 10.1016/j.egyr.2022.06.091.
J. Colquhoun and L. Partridge, “Computational Fluid Dynamics Applications in Hospital Ventilation Design,” Indoor and Built Environment, vol. 12, no. 1–2, pp. 81–88, Feb. 2003, doi: 10.1177/1420326X03012001013.
V. Tukaram. Joshi, “ Analyzing and Improving Thermal Comfort and Air Quality of Indoor Spaces Using CFD. ,” Disseteration, State University of New York, Buffalo, 2022.
J. P. Duguid and B. So, “The size and the duration of air-carriage of respiratory droplets and droplet-nuclei,” Epidemiol Infect, vol. 44, no. 6, pp. 471–479, 1946, doi: 10.1017/S0022172400019288.
A. Naseri, H. Emdad, S. Mehrabi, … S. S.-B. and, and undefined 2020, “Inhalability of micro-particles through the human nose breathing at high free-stream airflow velocities,” Elsevier, Accessed: Dec. 06, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0360132320303073
V. Stadnytskyi, C. E. Bax, A. Bax, and P. Anfinrud, “The airborne lifetime of small speech droplets and their potential importance in SARS-CoV-2 transmission,” Proc Natl Acad Sci U S A, vol. 117, no. 22, Jun. 2020, doi: 10.1073/PNAS.2006874117.
D. A. Edwards et al., “Inhaling to mitigate exhaled bioaerosols,” Proc Natl Acad Sci U S A, vol. 101, no. 50, pp. 17383–17388, Dec. 2004, doi: 10.1073/PNAS.0408159101.
P. Azimi, Z. Keshavarz, J. G. Cedeno Laurent, B. Stephens, and J. G. Allen, “Mechanistic transmission modeling of COVID-19 on the Diamond Princess cruise ship demonstrates the importance of aerosol transmission,” Proc Natl Acad Sci U S A, vol. 118, no. 8, Feb. 2021, doi: 10.1073/PNAS.2015482118.
J. Gupta, C. Lin, Q. C.-I. air, and undefined 2010, “Characterizing exhaled airflow from breathing and talking,” Wiley Online LibraryJK Gupta, CH Lin, Q ChenIndoor air, 2010•Wiley Online Library, vol. 20, no. 1, pp. 31–39, 2010, doi: 10.1111/j.1600-0668.2009.00623.x.
J. Gupta, C. Lin, Q. C.-I. air, and undefined 2009, “Flow dynamics and characterization of a cough,” Wiley Online LibraryJK Gupta, CH Lin, Q ChenIndoor air, 2009•Wiley Online Library, vol. 19, no. 6, pp. 517–525, 2009, doi: 10.1111/j.1600-0668.2009.00619.x.
K. Nissen et al., “Long-distance airborne dispersal of SARS-CoV-2 in COVID-19 wards,” Sci Rep, vol. 10, no. 1, Dec. 2020, doi: 10.1038/S41598-020-76442-2.
R. Zhang, Y. Li, A. L. Zhang, Y. Wang, and M. J. Molina, “Identifying airborne transmission as the dominant route for the spread of COVID-19,” Proc Natl Acad Sci U S A, vol. 117, no. 26, pp. 14857–14863, Jun. 2020, doi: 10.1073/PNAS.2009637117.
Y. Y. Zuo, W. E. Uspal, and T. Wei, “Airborne Transmission of COVID-19: Aerosol Dispersion, Lung Deposition, and Virus-Receptor Interactions,” ACS Nano, vol. 14, no. 12, pp. 16502–16524, Dec. 2020, doi: 10.1021/ACSNANO.0C08484.
M. Zhao et al., “Assessment of COVID-19 aerosol transmission in a university campus food environment using a numerical method,” Elsevier, Accessed: Dec. 06, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1674987122000068
Y. Shah, J. Kurelek, S. Peterson, S. Y.-P. of Fluids, and undefined 2021, “Experimental investigation of indoor aerosol dispersion and accumulation in the context of COVID-19: Effects of masks and ventilation,” pubs.aip.org, Accessed: Dec. 06, 2023. [Online]. Available: https://pubs.aip.org/aip/pof/article/33/7/073315/1076712
J. S.-I. air and undefined 2004, “On the history of indoor air quality and health,” academia.eduJ SundellIndoor air, 2004•academia.edu, Accessed: Dec. 06, 2023. [Online]. Available: https://www.academia.edu/download/40521122/On_the_history_of_indoor_air_quality_and20151130-12522-1weg0gt.pdf
C. ; Zhang, T. ; Yu, P. K. Heiselberg, M. Z. Pomianowski, and P. V Nielsen, “Diffuse ceiling ventilation: design guide,” 2016, Accessed: Dec. 06, 2023. [Online]. Available: https://vbn.aau.dk/en/publications/diffuse-ceiling-ventilation-design-guide
D. S. Thatiparti, U. Ghia, and K. R. Mead, “Computational fluid dynamics study on the influence of an alternate ventilation configuration on the possible flow path of infectious cough aerosols in a mock airborne infection isolation room,” Sci Technol Built Environ, vol. 23, no. 2, pp. 355–366, Feb. 2017, doi: 10.1080/23744731.2016.1222212.
M. Mirzaie, E. Lakzian, A. Khan, M. E. Warkiani, O. Mahian, and G. Ahmadi, “COVID-19 spread in a classroom equipped with partition – A CFD approach,” J Hazard Mater, vol. 420, p. 126587, Oct. 2021, doi: 10.1016/J.JHAZMAT.2021.126587.
C. K. Ho, “Modelling Airborne Transmission and Ventilation Impacts of a COVID-19 Outbreak in a Restaurant in Guangzhou, China,” Int J Comut Fluid Dyn, vol. 35, no. 9, pp. 708–726, Oct. 2021, doi: 10.1080/10618562.2021.1910678.
H. Motamedi, M. Shirzadi, Y. Tominaga, and P. A. Mirzaei, “CFD modeling of airborne pathogen transmission of COVID-19 in confined spaces under different ventilation strategies,” Sustain Cities Soc, vol. 76, p. 103397, Jan. 2022, doi: 10.1016/J.SCS.2021.103397.
H. T. Dao and K. S. Kim, “Behavior of cough droplets emitted from Covid-19 patient in hospital isolation room with different ventilation configurations,” Build Environ, vol. 209, p. 108649, Feb. 2022, doi: 10.1016/J.BUILDENV.2021.108649.
Y. Yan, X. Li, X. Fang, P. Yan, and J. Tu, “Transmission of COVID-19 virus by cough-induced particles in an airliner cabin section,” Engineering Applications of Computational Fluid Mechanics, vol. 15, no. 1, pp. 934–950, Jan. 2021, doi: 10.1080/19942060.2021.1922124.
M. El Hassan, H. Assoum, N. Bukharin, H. Al Otaibi, M. Mofijur, and A. Sakout, “A review on the transmission of COVID-19 based on cough/sneeze/breath flows,” The European Physical Journal Plus, vol. 137, no. 1, p. 1, Jan. 2022, doi: 10.1140/EPJP/S13360-021-02162-9.
R. Biswas, A. Pal, R. Pal, S. Sarkar, and A. Mukhopadhyay, “Risk assessment of COVID infection by respiratory droplets from cough for various ventilation scenarios inside an elevator: An OpenFOAM-based computational fluid dynamics analysis,” Physics of Fluids, vol. 34, no. 1, Jan. 2022, doi: 10.1063/5.0073694/2845895.
A. Issakhov, Y. Zhandaulet, P. Omarova, A. Alimbek, A. Borsikbayeva, and A. Mustafayeva, “A numerical assessment of social distancing of preventing airborne transmission of COVID-19 during different breathing and coughing processes,” Scientific Reports 2021 11:1, vol. 11, no. 1, pp. 1–39, May 2021, doi: 10.1038/s41598-021-88645-2.
C. Cravero and D. Marsano, “Simulation of COVID-19 indoor emissions from coughing and breathing with air conditioning and mask protection effects,” Indoor and Built Environment, vol. 31, no. 5, pp. 1242–1261, May 2022, doi: 10.1177/1420326X211039546/ASSET/IMAGES/LARGE/10.1177_1420326X211039546-FIG17.JPEG.
Z. Nie, Y. Chen, and M. Deng, “Quantitative evaluation of precautions against the COVID-19 indoor transmission through human coughing,” Scientific Reports 2022 12:1, vol. 12, no. 1, pp. 1–22, Dec. 2022, doi: 10.1038/s41598-022-26837-0.
A. Agrawal and R. Bhardwaj, “Reducing chances of COVID-19 infection by a cough cloud in a closed space,” Physics of Fluids, vol. 32, no. 10, p. 101704, Oct. 2020, doi: 10.1063/5.0029186/1059890.
Z. Zhang and Q. Chen, “Comparison of the Eulerian and Lagrangian methods for predicting particle transport in enclosed spaces,” Atmos Environ, vol. 41, no. 25, pp. 5236–5248, Aug. 2007, doi: 10.1016/j.atmosenv.2006.05.086.
V. D’Alessandro, M. Falone, L. Giammichele, and R. Ricci, “Eulerian-Lagrangian modeling of cough droplets irradiated by ultraviolet-C light in relation to SARS-CoV-2 transmission,” Physics of Fluids, vol. 33, no. 3, Mar. 2021, doi: 10.1063/5.0039224.
M. Saad, M. A. William, A. A. Hassan, and A. A. Hanafy, “Influence of air ceiling diffusers in enclosed spaces: An experimental and numerical investigation,” Energy Reports, vol. 9, pp. 59–71, Sep. 2023, doi: 10.1016/j.egyr.2023.05.253.
B. E. Launder and D. B. Spalding, “The numerical computation of turbulent flows,” Comput Methods Appl Mech Eng, 1974, doi: 10.1016/0045-7825(74)90029-2.
H. K. Versteeg and W. Malalasekera, An introduction to computational fluid dynamics: the finite volume method. Pearson Education, 2007.
J. P. Duguid, “THE SIZE AND THE DURATION OF AIR-CARRIAGE OF RESPIRATORY DROPLETS AND DROPLET-NUCLEI,” 1946.
M. K. Ahmadzadeh, “A numerical approach for preventing the dispersion of infectious disease in a meeting room,” 2022, doi: 10.21203/rs.3.rs-1803272/v1.
M. Li et al., “Towards realistic simulations of human cough: effect of droplet emission duration and spread angle,” Oct. 2022, doi: 10.1016/j.ijmultiphaseflow.2021.103883.
H. Nishimura, S. Sakata, and A. Kaga, “A New Methodology for Studying Dynamics of Aerosol Particles in Sneeze and Cough Using a Digital High-Vision, High-Speed Video System and Vector Analyses) A New Methodology for Studying Dynamics of Aerosol Particles in Sneeze and Cough Using a Digital High-Vision, High-Speed Video System and Vector Analyses,” PLoS One, vol. 8, no. 11, p. 80244, 2013, doi: 10.1371/journal.pone.0080244.
H. Li et al., “Airborne dispersion of droplets during coughing: a physical model of viral transmission,” Sci Rep, vol. 11, no. 1, Dec. 2021, doi: 10.1038/s41598-021-84245-2.
J. Muthusamy, S. Haq, S. Akhtar, M. A. Alzoubi, T. Shamim, and J. Alvarado, “Implication of coughing dynamics on safe social distancing in an indoor environment—A numerical perspective,” Build Environ, vol. 206, Dec. 2021, doi: 10.1016/j.buildenv.2021.108280.
H. Anzai et al., “Coupled discrete phase model and Eulerian wall film model for numerical simulation of respiratory droplet generation during coughing,” Sci Rep, vol. 12, no. 1, Dec. 2022, doi: 10.1038/s41598-022-18788-3.
M. Nicas, W. W. Nazaroff, and A. Hubbard, “Toward understanding the risk of secondary airborne infection: Emission of respirable pathogens,” J Occup Environ Hyg, vol. 2, no. 3, pp. 143–154, Mar. 2005, doi: 10.1080/15459620590918466.
J. Wei and Y. Li, “Enhanced spread of expiratory droplets by turbulence in a cough jet,” Build Environ, vol. 93, no. P2, pp. 86–96, Nov. 2015, doi: 10.1016/j.buildenv.2015.06.018.
R. A. Angelova, “Numerical simulation of the thermoregulation of clothed human body: skin and clothing temperatures,” Journal of the Brazilian Society of Mechanical Sciences and Engineering, vol. 37, no. 1, pp. 297–303, Jan. 2015, doi: 10.1007/s40430-014-0189-0.
A. Agrawal and R. Bhardwaj, “Reducing chances of COVID-19 infection by a cough cloud in a closed space,” Physics of Fluids, vol. 32, no. 10, Oct. 2020, doi: 10.1063/5.0029186.

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Can Airflow Manipulation Disrupt the Transmission of COVID-19 Variants and Highly Infectious Droplets?

Status:

Version 1

Abstract

Figures

1. Introduction

2. Materials and methods

3. Numerical assessment and visualization

4. Conclusions

Nomenclature

Acronyms:

Symbols:

Declarations

References

Additional Declarations

Status:

Version 1