Zonal model for predicting contaminant distribution in stratum ventilated rooms

Area of Science:

• Building ventilation systems
• Indoor air quality
• Computational fluid dynamics

Background:

• Accurate prediction of contaminant distribution is vital for designing effective stratum ventilation (SV) systems to minimize exposure risks.
• Zonal models offer a more practical approach compared to experimental methods or computational fluid dynamics (CFD) for ventilation analysis.

Purpose of the Study:

• To develop and validate a novel zonal model for predicting dynamic, non-uniform contaminant distribution in rooms utilizing stratum ventilation.
• To enhance the accuracy and convenience of contaminant exposure risk assessment in SV environments.

Main Methods:

• A zonal model was developed based on the distinct airflow patterns observed in stratum ventilation.
• The room was segmented into three zones: jet, entrainment, and mixing zones.
• Interzonal airflow rates were calculated using the supply air jet profile.

Main Results:

• The proposed zonal model effectively predicts dynamic contaminant distribution in stratum-ventilated rooms.
• Validation against experimental measurements showed good accuracy, with Mean Absolute Error (MAE) ranging from 0.51-2.36 ppm and Root Mean Squared Error (RMSE) from 0.64-2.53 ppm.
• The model's accuracy is influenced by the degree of mixing within each subzone.

Conclusions:

• The developed zonal model provides accurate predictions for non-uniform air distribution under stratum ventilation.
• This model demonstrates superior accuracy compared to existing zonal models for SV applications.
• The proposed method offers a valuable tool for optimizing SV system design and reducing contaminant exposure.