key: cord-0910135-0jdfc36h authors: Muthusamy, Jayaveera; Haq, Syed; Akhtar, Saad; Alzoubi, Mahmoud A.; Shamim, Tariq; Alvarado, Jorge title: Implication of coughing dynamics on safe social distancing in an indoor environment—A numerical perspective date: 2021-08-30 journal: Build Environ DOI: 10.1016/j.buildenv.2021.108280 sha: 473860b4818c09fb737b251507c320def046acce doc_id: 910135 cord_uid: 0jdfc36h Coughing is a primary symptomatic pathway of respiratory or air-borne disease transmission, including COVID-19. Several parameters such as cougher’s age, gender, and posture affect particle dispersion indoors. This study numerically investigates the transient cough evolution, contamination range, particle reach probability, and deposition fraction for different age groups of males and females in standing and sitting postures in a ventilated room. The efficacy of a cloth mask has also been studied with and without the influence of air ventilation. Validated Computational Fluid Dynamics methodology has been implemented to model complex physics such as turbulent buoyant cloud, particle-air interaction, particle collision/breakup, and droplet evaporation. Our results show that overall, the contamination range is slightly lower for females due to lower cough velocities and particle counts. Moreover, a significant fraction of particles crosses the two meters social distancing guideline threshold with an unhindered cough. Besides, wearing a cloth mask reduces the average contamination range by approximately one-third of the distance compared to coughing without the mask. However, aerosolized particles reach longer streamwise distances and drift for extended durations beyond thirty seconds. This study can be used to improve the heating, ventilation, and air conditioning recommendations and distancing guidelines in indoor settings. N i Cumulative number of particles deposited [-] n p Number of particles in a cell of continuous phase [-] N t Total number of particles deposited [-] p The recent outbreak of COVID-19 has resulted in a catastrophic impact 2 on the human condition. The respiratory illness has not only resulted in 3 infecting over 140 million people to date, but it has also adversely affected The droplet size distribution was found to have a key role in airborne and 44 droplet transmission. Diameters less than 10 [µm] were observed to be sus- 45 pended rather easily in the ambient air resulting in airborne transmission. 46 The ambient Relative Humidity (RH) and inlet cough velocity were also ob- where h is the enthalpy of the fluid and S t is the source term from the are applied as follows: where m p and v p are the mass and velocity of the particle and f d , f p , and f g 174 are drag, pressure and gravitational forces acting on the particle, respectively. Particles in turbulent flow are exposed to a randomly-fluctuating velocity tions is used to estimate the slip-velocity and employed to Lagrangian phase 184 models using particle Reynolds number. The drag force, f d in Eqn. (4), is modeled using the following equation: where C d is the drag coefficient of the particle and ρ is the density of the con- Energy conservation equation for the discrete phase is stated as: The effects of continuous phase on the discrete phase is also accounted for by enabling two-way coupling between Eulerian (air) and Lagrangian (droplets) phases. For an unsteady coughing process, the rate of momentum transfer from all particles in a cell, c, to the continuous phase is modeled using a source term in the continuous phase momentum equation, as follow: where ∆t is the time step size of the continuous phase, n p is the number of 190 particles in a cell of continuous phase whileṁ p is the mass transfer rate to 191 the particle. The convective source term in the energy equation is given by fraction occupied by these insoluble components is calculated to be 6.5 %. Particle evaporation is simulated using the quasi-steady multi-component 220 droplet evaporation model [41] . The evaporative mass transfer rate is com-221 puted by using the expression: where ε i is the fractional mass transfer rate of each component, B is the 223 Spalding mass transfer number, and g * is mass transfer conductance. The The effect of cloth masks on the spread of particles is also studied. The 293 cloth mask is modeled using an isotropic porous medium in the domain [47] . In this study, fifteen cases are simulated in total as outlined in Table 3 . These include six standing cases for each gender and age group, followed by Table 2 . Simulations are performed using the CFD solver StarCCM (V 15.02) con-376 sidering the parameters shown in The weighted average contamination range is defined as follows: where x i is the particle mass or particle count and d i is the distance traveled 478 by the particle in the streamwise direction from the mouth. During the transportation period, droplet could collide, resulting in coa- [µm] were observed. We hypothesize that the particle mass-weighted CR 488 is influenced by the large size (higher mass) particles, whereas the particle 489 count-weighted CR is impacted by the smallest size particles, as they are 490 higher in numbers. Hence, the CR is calculated using both particle mass lighter particles upwards, almost vertically, resulting in lower particle spread. The No AC, with mask case in Fig. 12 shows the condition, which includes being deposited on the ground within a short distance. To formally quantify this behavior, the concept of PRP is introduced. For a particle of a given diameter, d i , in the cough, the PRP can be defined as the likelihood of the particle being dispersed in the free-stream medium at a certain streamwise distance, x and time t. Mathematically this can be expressed as follows: where PRP i (x, t) is the particle reach probability of the diameter d i , diameter size is put in place to exclude the large particle sizes with low count. From the general trend seen in the Fig. 13 it is essential to pay close attention towards scenarios that would reduce 636 the airborne transmission. Fig. 15 An interactive web-based dashboard to 720 track COVID-19 in real time CDC, How COVID-19 spreads Identifying 726 airborne transmission as the dominant route for the spread of COVID-727 19 Modelling uncertainty in the relative risk of exposure to the SARS-731 CoV-2 virus by airborne aerosol transmission in well mixed indoor air Modelling airborne transmission of 734 COVID-19 in indoor spaces using an advection-diffusion-reaction equa-735 tion The mixing of airborne 737 contaminants by the repeated passage of people along a corridor Seasonal vari-740 ation in airborne infection risk in schools due to changes in ventilation 741 inferred from monitored carbon dioxide How 744 can airborne transmission of COVID-19 indoors be minimised? 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Mesh insensitivity study 869 Grid insensitivity study was performed to ensure the numerical results