A Digital-Twin and Machine-Learning Framework for Ventilation System Optimization for Capturing Infectious Disease Respiratory Emissions

Abstract

The pandemic of 2019 has led to an enormous interest in all aspects of modeling and simulation of infectious diseases. One central issue is the redesign and deployment of ventilation systems to mitigate the transmission of infectious diseases, produced by respiratory emissions such as coughs. This work seeks to develop a combined Digital-Twin and Machine-Learning framework to optimize ventilation systems by building on rapidly computable respiratory emission models developed in Zohdi (Comput Mech 64:1025–1034, 2020). This framework ascertains the placement and flow rates of multiple ventilation units, in order to optimally sequester particles released from respiratory emissions such as coughs, sneezes, etc. Numerical examples are provided to illustrate the framework.