摘要
Well-mixed zone models are often employed to compute indoor air quality and occupant exposures.While effective,a potential downside to assuming instantaneous,perfect mixing is underpredicting exposures to high intermittent concentrations within a room.When such cases are of concern,more spatially resolved models,like computational-fluid dynamics methods,are used for some or all of the zones.But,these models have higher computational costs and require more input information.A preferred compromise would be to continue with a multi-zone modeling approach for all rooms,but with a better assessment of the spatial variability within a room.To do so,we present a quantitative method for estimating a room’s spatiotemporal variability,based on influential room parameters.Our proposed method disaggregates variability into the variability in a room’s average concentration,and the spatial variability within the room relative to that average.This enables a detailed assessment of how variability in particular room parameters impacts the uncertain occupant exposures.To demonstrate the utility of this method,we simulate contaminant dispersion for a variety of possible source locations.We compute breathing-zone exposure during the releasing(source is active)and decaying(source is removed)periods.Using CFD methods,we found after a 30 minutes release the average standard deviation in the spatial distribution of exposure was approximately 28%of the source average exposure,whereas variability in the different average exposures was lower,only 10%of the total average.We also find that although uncertainty in the source location leads to variability in the average magnitude of transient exposure,it does not have a particularly large influence on the spatial distribution during the decaying period,or on the average contaminant removal rate.By systematically characterizing a room’s average concentration,its variability,and the spatial variability within the room important insights can be gained as to how much uncertainty is introduced into occupant exposure predictions by assuming a uniform in-room contaminant concentration.We discuss how the results of these characterizations can improve our understanding of the uncertainty in occupant exposures relative to well-mixed models.
基金
supported in parts by the U.S.Defense Threat Reduction Agency and performed under U.S.Department of Energy Contract No.DE-AC02-05CH11231.
