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.

The heat source of an air-conditioned room has an important effect on the indoor environment. The release rates of heat sources are related to the comfort of the designed thermal environment, so they must be determined. Traditional design methods rely on iterative guess-and-correct, which consumes resources and time and cannot meet the needs of modern design. This study aims to establish an inverse model of Tikhonov regularization and least square optimization by using computational fluid dynamics (CFD), so that researchers can accurately determine the time release rate of multiple heat sources with known parameters. The temporal release rates can then be solved based on the inverse matrix operation with the temperature series at different discrete times. The study speeds up the solving process and expresses the temperature as the convolution integral between the temperature response of the thermal response factor and the arbitrary release rate. The results show that applying the above method to the quantization of the temporal release rates of three heat sources in a three-dimensional cavity can correctly determine the temporal release rates of multiple heat sources. The errors between the inversely determined release rates and the actual release rates are less than 40%.