This project will model, analyze, predict, and present control mechanisms for a COVID-19 outbreak through an airborne infection in an aircraft cabin. As air travel resumes, it is expected that many passengers would be exposed to and possibly infected by the COVID-19 virus. As a result, there is an urgent need to rapidly develop solutions to determine the speed of the contagion by understanding the dynamics of the airflow inside aircraft. This research will combine four separate multi-physics models representing fluid dynamics, scalar transport, epidemiology, and airborne infection to analyze the spread of COVID-19 within a closed system such as an airplane. The multidisciplinary nature of this research will yield new algorithms at the interface of computational mathematics, deep learning, data science, epidemiology, and fluid dynamics and will provide novel techniques that can be directly applied to large-scale data to allow efficient and powerful data analysis. The project will also serve as valuable training for students. Open-source codes will be made available to the user community and will be open to contributions from end-users, academic researchers, industry members, practitioners, and government research labs. The research may also be extended to other physical spaces, such as marine vessels, trains, buses, or any other medium of public transportation systems.
This research will accomplish the following specific objectives (a) develop a fully 3-dimensional computational model capturing realistic geometry and coupling four different physical and biological systems; (b) implement a hidden multi-physics neural network framework to enable data assimilation and; (c) evaluate the predictive capability using simulated, experimental, and observational data in addition to developing and studying novel control and reinforcement learning mechanisms. The framework considers the characteristics of the exhalation of the droplets from COVID-19 infected members on an airplane that may not be wearing face masks, tracking the dispersion of these droplets, and tracking the inhalation of the droplets by susceptible passengers through these coupled multi-physics models. The research will help to develop a novel physics-informed deep-learning framework that will be capable of encoding the multi-physics system of equations modeled into the neural networks while being agnostic to the geometry or the initial and boundary conditions. Progress on the goals will provide advances in data-driven discovery, which will allow a better understanding of the impact of COVID-19.
This grant is being awarded using funds made available by the Coronavirus Aid, Relief, and Economic Security (CARES) Act supplement allocated to MPS.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
