Exploring the feasibility of predicting contaminant transport using a stand-alone Markov chain solver based on measured airflow in enclosed environments

Correctly predicting contaminant transport in enclosed environments is crucial for improving interior layouts to reduce infection risks. Using the measured airflow field as input to predict the contaminant transport may overcome the challenges of measuring complex boundary conditions and inaccurate turbulence modeling in the existing methods. Therefore, this study numerically explored the feasibility of predicting contaminant transport from the measured airflow field. A stand-alone Markov chain solver was developed so that the calculations need not rely on commercial software. Airflow information from CFD simulation results, including the three-dimensional velocity components and turbulence kinetic energy, was used as surrogate for experimental measurement based on the spatial resolution of ultrasonic anemometers. Three cases were used to assess the feasibility of the proposed method, and the calculation results were compared with the benchmark calculated by the commercial CFD software. The results show that, when the airflow was simple, such as that in an isothermal ventilated chamber, the stand-alone Markov chain solver based on the measured airflow field predicted the trend of contaminant transport and peak concentrations reasonably well. However, for complex airflow, such as that in non-isothermal chambers with heat sources or occupants, the solver can reasonably predict only the general trend of contaminant transport.