Real-time visualization of indoor aerosol dispersion based on a new Markov chain model

Accurate acquisition of real-time aerosol distribution indoors is crucial to both estimating real-time infection risk of indoor respiratory infectious diseases as well as rapidly optimizing the ventilation effectiveness of building structure during the design stage. Real-time prediction of aerosol distribution can hardly be achieved by CFD model due to its iterative solution strategy, while the Markov chain model can greatly reduce computing time by implementing the non-iterative state transfer process. In this study, a real-time visualization algorithm for aerosol dispersion in limited space is developed based on the Markov chain principle and pre-solved flow field. Then the reliability of the proposed algorithm is verified by experimental data. An interactive user interface is further constructed based on the validated algorithm to realize real-time simulation of the dynamic release process of multiple indoor pollution sources. Results show that the simulation outcomes agree well with the experimental validation data, and the dynamic real-time distribution of aerosols can be well visualized for steady-state airflow. The present study aims to provide a new effective prediction method for real-time visualization of indoor pollutant dispersion and rapid evaluation of the impacts of building structure on ventilation effectiveness.