Title of article :
Ventilation performance prediction for buildings: Model assessment
Author/Authors :
Qingyan Chen، نويسنده , , Kisup Lee، نويسنده , , Sagnik Mazumdar، نويسنده , , Stephane Poussou، نويسنده , , Liangzhu Wang، نويسنده , , Miao Wang، نويسنده , , Zhao Zhang، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2010
Abstract :
Designing ventilation systems for buildings requires a suitable tool to assess the system performance. This investigation assessed seven types of models (analytical, empirical, small-scale experimental, full-scale experimental, multizone network, zonal, and CFD) for predicting ventilation performance in buildings, which can be different in details according to the model type. The analytical model can give an overall assessment of a ventilation system if the flow could be approximated to obtain an analytical solution. The empirical model is similar to the analytical model in terms of its capacities but is developed with a database. The small-scale model can be useful to examine complex ventilation problems if flow similarity can be maintained between the scaled model and reality. The full-scale model is the most reliable in predicting ventilation performance, but is expensive and time consuming. The multizone model is a useful tool for ventilation design in a whole building, but cannot provide detailed flow information in a room. The zonal model can be useful when a user has prior knowledge of the flow in a room. The CFD model provides the most detailed information about ventilation performance and is the most sophisticated. However, the model needs to be validated by corresponding experimental data and the user should have solid knowledge of fluid mechanics and numerical technique. Thus, the choice for an appropriate model is problem-dependent.
Keywords :
analytical model , Full-scale experimental model , Empirical model , Small-scale experimental model , Zonal/nodal model , Multizone model , CFD
Journal title :
Building and Environment
Journal title :
Building and Environment