EAGER: Modeling and Control of COVID-19 Transmission in Indoor Environments

Although much remains unknown about COVID-19, observational studies have demonstrated that tiny coronavirus particles can become airborne and hence, remain aloft in air for a few hours and travel distances much longer than the 6-feet guidance of the social distancing. This consequently increases the risk of exposure to the virus in confined indoor spaces, such as, restaurants, offices, and dormitories. Although they are generally not designed for pandemic situations, heating, ventilation, and air-conditioning (HVAC) systems, if properly designed and controlled, can significantly mitigate the spread of diseases in indoor spaces. This collaborative research project between MIT and Mitsubishi Electric Research Laboratories (MERL) synergies research activities in control theory, fluid dynamics, and machine learning to enable optimal design and control of HVAC systems for built environments so as to minimize the exposure risk of occupants. The project will establish a new theoretical framework for optimal control of HVAC systems employing realistic computational models of the disease transmission as well as the indoors thermofluid dynamics. This will provide business owners and building managers with general guidelines for the HVAC operations.

The research will integrate the proposed framework with data-driven, reduced-order models and employ reinforcement learning techniques to develop computationally tractable algorithms for adaptive, online control of the HVAC systems in order to contain the spread of the coronavirus. First, the project will resolve the uncertainty that exists about the minimum level of modeling complexity required for developing reliable, COVID-19 safety guidelines by systematic study of different levels of modeling complexities and analyzing their effects on the transport of pathogen-laden droplets and aerosols. Second, the project will develop an optimal control framework that studies the flow physics of the virus transmission and the general airflow in the indoor space. Third, this research will develop a data driven, adaptive control framework that can be used to design plug and play controllers for HVAC systems. The proposed research will have significant social and economic benefits by setting the foundation for an improved methodology in designing case-specific infection control guidelines that can be realized by affordable HVAC devices, such as, ventilators and fans.

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.