Selection of window sizes for optimizing occupational comfort and hygiene based on computational fluid dynamics and neural networks

The present paper presents a novel computational method to optimize window sizes for thermal comfort and indoor air quality in naturally ventilated buildings. The methodology is demonstrated by means of a prototype case, which corresponds to a single-sided naturally ventilated apartment. Initially, the airflow in and around the building is simulated using a Computational Fluid Dynamics model. Local prevailing weather conditions are imposed in the CFD model as inlet boundary conditions. The produced airflow patterns are utilized to predict thermal comfort indices, i.e. the PMV and its modifications for non-air-conditioned buildings, as well as indoor air quality indices, such as ventilation effectiveness based on carbon dioxide and volatile organic compounds removal. Mean values of these indices (output/objective variables) within the occupied zone are calculated for different window sizes (input/design variables), to generate a database of input–output data pairs. The database is then used to train and validate Radial Basis Function Artificial Neural Network input–output “meta-models”. The produced meta-models are used to formulate an optimization problem, which takes into account special constraints recommended by design guidelines. It is concluded that the proposed methodology determines appropriate windows architectural designs for pleasant and healthy indoor environments.