Reconstruction of airflow path parameters in multizone models based on Bayesian inference and measured data

The accurate inference of model parameters is important for predicting contaminant distributions in multizone environments, which is critical for ensuring indoor personnel health and formulating an optimistic ventilation strategy. However, several important airflow path parameters (flow coefficients, flow exponents, etc.) are not directly observed or observable. Therefore, in this study, we proposed a reconstruction method to estimate the parameters for the multizone model based on measured data. MATLAB codes were programmed to call the CONTAM (a multizone simulation program developed by the US National Institute of Standards and Technology (NIST)) engine to simulate the pollutant transport in a multizone scaled building model. The sensitivities of different model parameters were evaluated through a Global Sensitivity Analysis (GSA). Subsequently, a Bayesian inference algorithm was applied to iteratively determine the unknown parameters. Finally, multizone scaled experiments with different forms of pollutant sources were conducted to validate the reconstruction method. The pollutant concentrations predicted using the measured parameters were compared with the those obtained using the reconstructed parameters. The results showed that the concentrations predicted using the reconstructed parameters agreed well with the measured data under constant source (CS) release and most dynamic source (DS) release in different experimental cases. The simulation results based on the reconstructed parameters were more accurate than those based on the directly measured parameters. The proposed reconstruction method can conveniently construct digital models that are equivalent to physical buildings, and these models can be further used to conduct analysis of built environments.