A grid-merging operation to accelerate the Markov chain model in predicting steady-state and transient transmission of airborne particles

To accelerate the process of obtaining the faster-than-real-time information for both steady-state and transient particle transmission in the indoor or local atmospheric environment, a grid-merging operation has been developed as applying the Computational Fluid Dynamics (CFD) combined Markov chain model. A steady-state flow field was calculated in advance, and then the flow rate data were exported into MATLAB platform and preprocessed with matrixing process. The grid-merging operation combined Markov chain model therefore was realized in a computational resource saving way. Two particle transmission cases including both a constant particle releasing source and a pulsed particle releasing source were used to validate the simulation results, and the general trends of the particle concentration distributions agreed reasonably well with the experimental data. In addition, the computing time costs after the grid-merging operation can remarkably be reduced while maintaining an acceptable accuracy. Besides, it is crucial for the overall computing accuracy to select one appropriate time step size Δt for as many cells as possible within the whole computational domain.