首页|Comparison of models to predict air infiltration rate of buildings with different surrounding environments
Comparison of models to predict air infiltration rate of buildings with different surrounding environments
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The air infiltration rate of buildings strongly influences indoor environment and energy consumption.In this study,several traditional methods for determining the air infiltration rate were compared,and their accuracy in different scenarios was examined.Additionally,a method combining computational flow dynamics(CFD)with the Swami and Chandra(S-C)model was developed to predict the influence of the surrounding environment on the air infiltration rate.Two buildings in Dalian,China,were selected:one with a simple surrounding environment and the other with a complex surrounding environment;their air infiltration rates were measured.The test results were used to validate the accuracy of the air infiltration rate solution models in different urban environments.For the building with a simple environment,the difference between the simulation and experimental results was 0.86%-22.52%.For the building with a complex environment,this difference ranged from 17.42%to 159.28%.We found that most traditional models provide accurate results for buildings with simple surrounding and that the simulation results widely vary for buildings with complex surrounding.The results of the method of combining CFD with the S-C model were more accurate,and the relative error between the simulation and test results was 10.61%.The results indicate that the environment around the building should be fully considered when calculating the air infiltration rate.The results of this study can guide the application of methods of determining air infiltration rate.
air infiltration rate modelsCFD simulationbuilding surroundingair infiltration rate test
Shu Zheng、Xiujiao Song、Lin Duanmu、Yu Xue、Xudong Yang
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Department of Building Science,Tsinghua University,Beijing 100084,China
School of Civil Engineering,Dalian University of Technology,Dalian 116024,China
Shanxi Research Institute for Clean Energy,Tsinghua University,Taiyuan 030032,China
National Natural Science Foundation of ChinaTsinghua-Toyota Joint Research Institute Interdisciplinary Program
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