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Dynamical systems and Machine Learning approach to mixing and dispersion of airborne contaminants in indoor environments

Academic lead
Dr Amirul Khan, School of Civil Engineering,
Prof Steve Tobias, School of Mathematics,, Prof Catherine Noakes, School of Civil Engineering,, Dr Gareth Keevil, School of Earth & Environment,
Project themes
Biomedical Flows, Environmental Flows, Underpinning Methods for Fluid Dynamics

Understanding the effect of airflow in enclosed/indoor environments is of great interest due to its close relationship to occupant’s health, thermal comfort, and energy efficiency. Optimally designed ventilation could result in increased comfort and reduced health risk of the occupants. Airflow indoors can distribute pathogen-laden aerosols or pollutants and can pose a significant health hazard. Furthermore, indoor airflow patterns can be very complicated, and the complex and nonlinear nature of the problem makes it challenging to formulate a general mathematical framework which could be utilised by engineers to design and operate these environments optimally.  

This project aims to develop a generalised mathematical framework for ventilated indoor environments using ideas from dynamical systems theory and machine learning (data-driven) approaches to quantify mixing which will impact the design and estimation of health risks in these environments.