Predicting transient particle transport in enclosed environments based on Markov chain

Quick information of airborne infectious disease transmission in enclosed environments is critical to reduce the risk of infection of occupants. This study developed a combined CFD and Markov chain method for quickly predicting the transient particle transport in enclosed environments. The method firstly calculated a transition probability matrix using CFD simulation. Then the Markov chain technique was applied to calculate the transient particle concentration distribution. This investigation used three cases, particle transport in an isothermal clean room, an office with Under-Floor Air-distribution system and the first-class cabin of an MD-82 airliner, to validate the combined CFD and Markov chain method. The transient particle concentration distribution predicted by the Markov chain method reasonably agreed with the CFD simulation for these cases. The proposed Markov chain method can provide faster-than-real-time information of particle transport in enclosed environments. Furthermore, for a fixed airflow field, when the source location is changed, the Markov chain method can avoid the re-calculations of the particle equations and thus reduce the computing cost.