LAUSR

Airflow network (AFN) model embedded in building energy simulation (BES) tools such as EnergyPlus is extensively used for prediction of cross-ventilation in buildings. The noticeable uncertainty in the measurement of the surface pressure, discharge coefficient, and simplifications applied to the orifice-based equation result in considerable discrepancies in the prediction of the cross-ventilation airflow rate values. Computational Fluid Dynamics (CFD) provides more accurate results comparing to the orifice-based equations although with an excessive computational cost. The aim of this study is, therefore, to improve the accuracy of the orifice-based model by development of an adaptive correlation for the discharge coefficient using CFD. Hence, a validated CFD model for the cross-ventilation of an unsheltered building is firstly developed using an experimental study. In the next step, by exploiting Latin hypercube sampling (LHS) approaches, a large CFD dataset of 750 scenarios for different building geometries (i.e. square cube, cuboid and long corridor) is generated; the dataset is then coupled to the AFN crossventilation model to obtain an adaptive correlation for the discharge coefficient as a function of the openings’ geometries and location using response surface (RSM) and radial basis function (RBF) models. Results show that the newly developed adaptive correlation successfully increases the accuracy of AFN model for the cross-ventilation modeling of unsheltered buildings as the relative errors for the airflow rate prediction of different building geometries are significantly decreased up to 28% in comparison with the cases with constant discharge coefficient and surface-averaged and local-surface wind pressure coefficients. Results, also demonstrate the importance of considering the value of the local-surface wind pressure in the AFN model for the square cube and cuboid building models.