The coronavirus disease 2019 (COVID-19) outbreak has altered the lives of everyone on a global scale due to its high transmission rate. In the current work, the droplet dispersion and evaporation originated by a cough at different velocities is studied. A multiphase computational fluid dynamic model based on fully coupled Eulerian–Lagrangian techniques was used. The evaporation, breakup, mass transfer, phase change, and turbulent dispersion forces of droplets were taken into account. A computational domain imitating an elevator that with two individuals inside was modeled. The results showed that all droplets smaller than 150 μm evaporate before 10 s at different heights. Smaller droplets of <30 µm evaporate quickly, and their trajectories are governed by Brownian movements. Instead, the trajectories of medium-sized droplets (30–80 µm) are under the influence of inertial forces, while bigger droplets move according to inertial and gravitational forces. Smaller droplets are located in the top positions, while larger (i.e., heaviest) droplets are located at the bottom.

Turbulence in fluids has been a popular research topic for many years due to its influence on a wide range of applications. Computational Fluid Dynamics (CFD) tools are able to provide plenty of information about this phenomenon, but their computational cost often makes the use of these tools unfeasible. For that reason, in recent years, turbulence modelling using Artificial Neural Networks (ANNs) is becoming increasingly popular. These networks typically calculate directly the desired magnitude, having input information about the computational domain. In this paper, a Convolutional Neural Network (CNN) for predicting different magnitudes of turbulent flows around different geometries by approximating the equations of the Reynolds-Averaged Navier-Stokes (RANS)-based realizable k-ε two-layer turbulence model is proposed. Using that CNN, alternative network structures are proposed to predict the velocity fields of a turbulent flow around different geometries on a rectangular channel, with a preliminary stage to predict pressure and vorticity fields before calculating the velocity fields, and the obtained results are compared with the ones obtained with the basic structure. The results demonstrate that the proposed structures clearly outperform the basic one, especially when the flow becomes uncertain. In addition, considering the results, the best network configuration is proposed. That network is tested with a domain with multiple geometries and a domain with a narrowing of the channel, which are domains with different conditions from the training ones, showing fairly accurate predictions.

The conduct of respiratory droplets is the basis of the study to reduce the spread of a virus in society. The pandemic suffered in early 2020 due to COVID-19 shows the lack of research on the evaporation and fate of droplets exhaled in the environment. The current study, attempts to provide solution through computational fluid dynamics techniques based on a multiphase state with the help of Eulerian–Lagrangian techniques to the activity of respiratory droplets. A numerical study has shown how the behavior of droplets of pure water exhaled in the environment after a sneeze or cough have a dynamic equal to the experimental curve of Wells. The droplets of saliva have been introduced as a saline solution. Considering the mass transferred and the turbulence created, the results has showed that the ambient temperature and relative humidity are parameters that significantly affect the evaporation process, and therefore to the fate. Evaporation time tends to be of a higher value when the temperature affecting the environment is lower. With constant parameters of particle diameter and ambient temperature, an increase in relative humidity increases the evaporation time. A larger particle diameter is consequently transported at a greater distance, since the opposite force it affects is the weight. Finally, a neural network-based model is presented to predict particle evaporation time.

The protection provided by wearing masks has been a guideline worldwide to prevent the risk of COVID-19 infection. The current work presents an investigation that analyzes the effectiveness of face shields as personal protective equipment. To that end, a multiphase computational fluid dynamic study based on Eulerian–Lagrangian techniques was defined to simulate the spread of the droplets produced by a sneeze. Different scenarios were evaluated where the relative humidity, ambient temperature, evaporation, mass transfer, break up, and turbulent dispersion were taken into account. The saliva that the human body generates was modeled as a saline solution of 8.8 g per 100 mL. In addition, the influence of the wind speed was studied with a soft breeze of 7 km/h and a moderate wind of 14 km/h. The results indicate that the face shield does not provide accurate protection, because only the person who is sneezed on is protected. Moreover, with a wind of 14 km/h, none of the droplets exhaled into the environment hit the face shield, instead, they were deposited onto the neck and face of the wearer. In the presence of an airflow, the droplets exhaled into the environment exceeded the safe distance marked by the WHO. Relative humidity and ambient temperature play an important role in the lifetime of the droplets.

The air quality is a parameter to be controlled in order to live in a comfortable place. This paper analyzes the trajectory of aerosols exhaled into the environment in a classroom. Three scenarios are investigated; without ventilation, with natural and with mechanical ventilation. A multi-phase computational fluid study based on Eulerian-Lagrangian techniques is defined. Temperature and ambient relative humidity, as well as air velocity, direction and pressure is taken into account. For droplets evaporation, mass transfer and turbulent dispersion have been added. This work tends to be of great help in various areas, such as the field of medicine and energy engineering, aiming to show the path of aerosols dispersed in the air. The results show that the classroom with a mechanical ventilation scheme offers good results when it comes to an efficient control of aerosols. In all three cases, aerosols exhaled into the environment impregnate the front row student in the first 0.5 s. Reaching the time of 4, 2 and 1 s, in the class without ventilation, mechanical and natural ventilation, respectively, the aerosols have been already deposited on the table of the person in the first row, being exposed for longer in the case of no ventilation. Particles with a diameter of less than 20 μm are distributed throughout the classroom over a long period. The air jet injected into the interior space offers a practically constant relative humidity and a drop in temperature, slowing down the process of evaporation of the particles. In the first second, it can be seen that a mass of 0.0025 mg formed by 9 million droplets accumulates, in cases without ventilation and natural ventilation. The room with a mechanical installation accumulated 5.5 million particles of mass 0.0028 mg in the first second. The energy losses generated by natural ventilation are high compared to the other scenarios, exactly forty and twenty times more in the scenario with mechanical ventilation and without ventilation, respectively.

The global COVID-19 and its variants put us on notice of the importance of studying the spread of respiratory diseases. The most common means of propagation was the emission of droplets due to different respiration activities. This study modeled by computational fluid dynamics (CFD) techniques a high risk scenario like a hospital elevator. The cabin was provided with an extraction fan and a rack for air renewal. Inside, a sneeze, a cough and a continuum speech were simulated. Inside the lift, two occupants were introduced to observe the risk of propagation of emitted droplets and the impact in diseases spreading risk. The fan effectivity over the droplets ejection was analyzed, as well as environmental condition of a clinical setting. For this purpose the amount of droplets inside were counted during whole time of simulations. The effect of the fan was concluded as able to eject the 60% of small droplets, but also a high performance in spreading particles inside. Among the three cases, the riskiest scenario was the continuum speech due to the saturation of droplets in airborne.