A new interactive tool calculates the risk of catching COVID-19 indoors from the droplets and aerosols people produce when breathing, talking, coughing, and sneezing. As winter approaches, buildings will limit ventilation for comfort, potentially trapping infectious particles indoors. Current methods cannot assess this risk quickly for specific rooms. The tool combines computational fluid dynamics, experimental data, and artificial intelligence to estimate droplet and aerosol concentrations in near real-time. It accounts for room volume, ventilation, number of occupants, and the effect of face masks. A datahub will store and manage experimental results, numerical simulations, and existing data from other projects. Reduced-order models will allow the system to estimate risk for scenarios not directly tested, at low computational cost. If successful, the tool could help managers of offices, schools, shops, and other indoor spaces make rapid, evidence-based decisions about occupancy limits, ventilation needs, and mask requirements. This would reduce infection risk without relying on generic rules of thumb, and could remain useful beyond the current pandemic for managing airborne disease transmission in crowded indoor environments.
View original technical description
This project brings together unique expertise in Computational and Experimental Fluid Dynamics, Model Reduction and Artificial Intelligence, to identify solutions for the management of people and spaces in the current pandemic and post lockdown. A new interactive tool is proposed that evaluates the risk of infection in the indoor environment from droplets and aerosols generated when breathing, talking, coughing and sneezing. This capability will become more critical as winter approaches and building ventilation will need to be limited for comfort considerations. The fluid dynamic behaviour of droplets and aerosols, the effect of using face masks as well as other parameters such as room volume, ventilation and number of occupants are considered. A datahub capable of storing, curating and managing heterogeneous data from sources internal and external to the project will be created. A synergetic experimental and numerical approach will be undertaken. These will complement the existing literature and data from other EPSRC-funded projects providing suitable datasets with adequate resolution in time and space for all the relevant features. To support experiments and numerical simulations, reduced order models capable of interpolating and extrapolating the scenarios collected in the database will be used. This will permit the estimation of droplet and aerosol concentrations and distributions in unknown scenarios at low-computational cost, in near real-time. A state-of-the-art AI-based framework, incorporating descriptive, predictive and prescriptive techniques will extract the knowledge from the data and drive the decision-making process and provide in near real-time the assessment of risk levels.
Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.
Is something wrong? Let us know