NSF Award
2406129
This Smart and Connected Health (SCH) award will support research that advances national health, prosperity, and welfare by investigating the potential of Heating, Ventilation, and Air Conditioning (HVAC) systems to mitigate the spread of airborne viruses such as SARS-CoV-2 and other pollutants in school classrooms. Due to the nature of the indoor classroom environment, school-aged children are particularly vulnerable to infectious diseases, and most current HVAC systems are not optimized to effectively prevent cross-infections. This research project combines a computational model that captures the effects of airflow on viral transport, uptake, and immune response with a generative Artificial Intelligence (AI) model trained by laboratory data and simulation experiments to improve design and real-time control of air handling technology. By optimizing HVAC systems to minimize infection risks, the project plans to contribute to healthier indoor environments, reducing the incidence of disease transmission and improving overall public health outcomes. <br/><br/>The goal of this research project is to develop a robust, multiscale computational model to understand the relationship between HVAC design, indoor airflow, virus emission, transmission, and infection risks among children in representative indoor environments. Specifically, the research objectives are to: (1) determine the spatiotemporal concentration distribution of pollutant- and virus-laden aerosols in classrooms with various layouts and children’s respiratory systems, using a model that combines Computational Fluid Dynamics (CFD) and Host Cell Dynamics (HCD) to generate infection risk indices that guide HVAC system design optimization; and (2) develop a generative AI-empowered tool for efficient HVAC design and real-time control to mitigate infection risks. The computational model aims to predict virus-laden aerosol transport, distribution, and infection risks from emission sites to children’s respiratory system under multiple HVAC configurations. The generative AI model plans to deploy generative adversarial networks (GAN) and diffusion models for the design and optimization of HVAC systems, reducing computational costs and enhancing design efficiency. This project leverages the interdisciplinary expertise of the research team to with the intent of creating a transformative tool for public health enhancement. The project includes outreach to engage K-12 students, educators, and the broader community, raising awareness about the importance of indoor air quality and the role of advanced technologies in public health. Additionally, the project provides interdisciplinary training opportunities for students and researchers in engineering, computer science, data science, and public health, promoting diversity and inclusion in these fields.<br/><br/>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.
