Ventilation performance and optimization of building-integrated wind catchers in dense urban canyons with adjacent window openings

Abstract

Natural ventilation in dense urban street canyons is often constrained by low wind speeds, flow recirculation, and turbulent air movement, resulting in poor indoor air quality and thermal comfort. Wind catchers offer a passive ventilation strategy by capturing higher-altitude winds and directing fresh air into buildings. This study investigates the ventilation performance of buildings equipped with wind catchers using Computational Fluid Dynamics (CFD). The numerical model was validated through wind tunnel experiments, and grid sensitivity analysis was conducted to ensure computational accuracy. The effects of wind catcher rooftop location, height, and diameter on airflow patterns, pressure distribution, and ventilation performance were systematically examined. The results show that both the placement and geometric characteristics of wind catchers significantly influence indoor airflow distribution and air quality. To determine the most effective design, Grey Relational Analysis was employed by considering age of air, predicted mean vote (PMV), and wind catcher size as performance criteria. The optimized wind catcher configuration reduced the age of air and PMV by 68% and 28%, respectively, compared with a building without a wind catcher. Although occupying only 2.6% of the building volume, the optimized wind catcher substantially enhanced indoor ventilation and thermal comfort. Practical application Our article presents optimized wind catcher ventilator configurations and offers a practical passive ventilation strategy for high-density urban buildings affected by restricted airflow within street canyons. Improved indoor airflow and reduced PMV can enhance thermal comfort and reduce dependence on mechanical cooling. We present CFD-based results that provide quantitative guidance for selecting wind catcher dimensions and rooftop placement, while the compact configurations also support retrofit applications for existing buildings. Our multi-objective optimization framework further enables balanced design decisions by simultaneously considering indoor air quality, thermal comfort, and structural size.