Prediction of transient particle transport in transient indoor airflow by integrated fast fluid dynamics and Markov chain model

It is crucial to accurately and efficiently predict transient particle transport in indoor environments in order to improve air distribution design and reduce health risks. For the steady-state indoor airflow, our previous studies have found that the integrated fast fluid dynamics (FFD) + Markov chain model increased the speed of calculation by around seven times compared to the combination of computational fluid dynamics (CFD) + Eulerian model and CFD + Lagrangian model, while achieving the same level of accuracy. However, the indoor airflow could be transient, if there was a human behaviour involved like coughing or sneezing, opening the door, and supplying the air periodically. Therefore, this study developed an FFD + Markov chain model solver for predicting transient particle transport in transient indoor airflow in OpenFOAM. This investigation used transient particle transport in a ventilated two-zone chamber to validate the model. In this case, the validation used experimental data from literature and showed that the predicted particle concentration by FFD + Markov chain model matched well with the experimental data. Besides, it had similar accuracy as the FFD + Eulerian model and the CFD + Eulerian model. The FFD + Markov chain model requires a similar computational time with the FFD + Eulerian model if the same time step size was used.