In this project, the spread of the coronavirus is simulated when two people talk without a mask. Simulating the coronavirus has become crucial in the wake of the global pandemic. As cities and countries worldwide grapple with the outbreak, scientists are turning to computer simulations to understand the spread of the virus, identify potential treatments, and develop strategies for slowing its transmission. Computer simulations involve using mathematical models to create virtual representations of real-world scenarios. For the coronavirus, simulations can help researchers track the virus’s transmission, predict the impact of various interventions, and design new drugs or vaccines.
One important use of simulations is identifying patterns of high risk for transmission. Simulations help scientists to understand how the virus spreads from person to person and how quickly it can infect large populations. This information is vital in planning and implementing measures to prevent the spread of the virus. Another use of simulations is in evaluating the effectiveness of various interventions. Scientists can use modeling to test different scenarios, such as social distancing, quarantine, or mask-wearing. By comparing the outcomes of these simulations, researchers can determine which measures are most effective at slowing the spread of the virus.
Additionally, simulations can help in the development of new treatments and vaccines. Scientists can use computer simulations to predict how different drugs or vaccines may interact with the virus. This allows for faster development of potential solutions and reduces the need for extensive trial and error in the lab. Computer simulations provide scientists with valuable information about how the virus spreads, how to prevent transmission, and how to develop new therapies. As the pandemic continues to evolve, simulations will likely play an essential role in helping to understand and combat this disease.
In this project, the discrete phase model in Fluent has been used to model the spread of the coronavirus. The particle injection type is a solid cone with a cone angle of 30 degrees and an outer diameter of 0.004 meters. The particle release velocity is also assumed to be 15 meters per second. The Rosin-Lammer method has been used to distribute the size of the dispersed particles.
The geometry of the project has been designed using Space Claim software. The meshing of this geometry has been performed using Ansys Meshing software, and the number of elements used in this project is 549,339.
The SIMPLE method has been used to couple the velocity and pressure variables in the equations.
After simulation, we can observe and track the movement path of the coronavirus. We see that not using a mask can rapidly spread the virus and infect other people.