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Evidence of Airborne Transmission of the Severe Acute Respiratory Syndrome Virus
Here is a very good scientific article about Evidence of Airborne Transmission of the Severe Acute Respiratory Syndrome Virus. It may be relevant to coronavirus research and use of computational fluid dynamics CFD.
Computational Fluid Dynamics and Fight Against COVID19
Computational Fluid Dynamics or CFD is more important then ever in a fight against the COVID 19 Pandemic. Its possible to simulate various means of the transmission of the disease through air using CFD simulations. For example, CIMNE researchers are applying computational fluid dynamics models coupled with particle-based models to the simulation of the flow of virus in the air produced by a sneeze. This research is carried out in close cooperation with the group of Professor Rainald Lohner at George Mason University in the USA. Prof Lohner, who is also the PI in this research activity, is an affiliated scientist to CIMNE where he spends 2-3 months research periods every year since 1995.
These virus flow simulations will be useful for predicting the distribution of virus in the air in closed and open environments, such as hospitals and supermarkets, among others. These predictions will be more relevant in highly populated spaces, such as airports or crowded hospitals.
The problem is particularly important to face the challenges that the Covid-19 crisis is posing to society. Many doctors, nurses, TSA personnel etc. can be infected needlessly because airflow is poorly understood or neglected. In order for the virus not to spread through medical facilities infecting people, the airflow needs to be managed so that no particles from rooms with Codiv-19 patients leave those rooms.
Furthermore, by being able to predicting how the airflow inside high-risk rooms is, the medical staff could position themselves so that the chances of infection are minimized. By using simulation technology these flows can be computed and the airflow can be optimized so that further infections are minimized.