Prediction Of Flow Resistance And Airflow Rate In A Breathing Wall

As a novel mode to intake outdoor air for building ventilation, breathing walls have gained significant concerns. Breathing walls can simultaneously provide thermal insulation, ventilation and air cleaning. As compared with the energy-consuming mechanical ventilation, breathing walls have large potentials in conserving energy and improving indoor environment. The driven force for air motion through breathing walls can be thermal buoyancy, wind pressure as well as mechanical fans if needed. To optimize the breathing wall design, the quantitative relation between flow resistance and airflow rate must be determined. The induced airflow rate through breathing walls can impact the performance of thermal insulation and air filtration. This thesis outlines some attempts to develop a fast correlation model and a dedicate computational fluid dynamics (CFD) model to predict the airflow rates by breathing walls. These models can guide towards to the optimal design of breathing walls.The correlation model was firstly established based on energy conservation and pressure balance laws. The solar irradiation intensity, wind pressure and porous filter geometric sizes are inputted to the models as the known parameters. As a comparison, the CFD modeling by adopting the RNG k-ε turbulence model and the enhanced wall treatment was also applied to model the breathing walls. For simplicity, the penetration flow through the porous filtration unit is resolved by the Brinkman-Forchheimer extended Darcy model. To validate both types of computational models, the prototype of a breathing wall model is constructed in lab. Both pressure and induced airflow rates are measured by a high-precision micro manometer. The results obtained by the two computational models are compared against the experiment data to verify the model capabilities and accuracy.It is found that the CFD model is able to predict airflow rates in good agreement with the measurement data. The fine distribution of pressure and airflow rates within the whole wall can be solved by the CFD model. The correlation model also provides reasonably accurate results, in which the deviations between the prediction and the measurement are found falling within the range of measurement uncertainties. Because no iteration is needed in solving the correlation model, the correlation is very efficient. Besides, both types of models can take into account various internal structure parameters and external environment conditions, which makes them useful for optimizing the system design.The results also show that the solar irradiation intensity varies cubically with the induced airflow rate under the single driven force of thermal buoyancy; while the quadratic relation is maintained between the wind pressure and the airflow rate if the only driven force is the outdoor wind. When both wind and thermal buoyancy coexist, the flow resistance versus airflow rate depends on the relative strength of each driven force. As compared with the winter condition, the breathing wall system in summer can induce larger airflow rate, which is of help to enhance the summer ventilation rate.