Reconstruction of uncertain parameters in a multizone model based on contam and bayesian inference

Abstract

The prediction of contaminant distribution in multi-zone environment is critical for ensuring indoor personnel health and making an optimistic ventilation strategy. However, the input of uncertainty parameters (flow coefficients, flow exponents, etc.) has a significant impact on the predicted pollutant concentrations. In this study, we proposed a reconstruction method to achieve parameter estimation for the multi-zone model. MATLAB codes was programmed to call CONTAM engine to accomplish pollutant transport simulation in a multi-zone scaled building model. Then a Bayesian inference algorithm compiled in MATLAB codes was applied to determine the unknown parameters iteratively. Finally, multi-zone scaled experiments with different forms of pollutant sources were employed to validate the reconstruction method. The results showed that the predicted concentrations with the reconstructed parameters agreed well with the measured data in the constant source (CS) experiment. While, for the dynamic source (DS) experiment, the predicted concentrations had some discrepancies with the measured data.