Parametric study of energy optimization and airflow management in high-rise buildings with double-skin façade using a genetic algorithm and CFD

This study investigates Energy Use Intensity (EUI) optimization through airflow management in a hypothetical high-rise office building in Istanbul, Türkiye. It focuses on a representative floor with a naturally ventilated Double Skin Façade (DSF), incorporating air-driven conduits to address ventilation challenges. The study's significance lies in enhancing fresh air provision while optimizing energy efficiency. The aim is to improve natural airflow on upper floors, where ventilation challenges arise due to wind flow characteristics, while reducing energy consumption. A parametric model was developed in Rhino-Grasshopper, integrating energy simulation via Honeybee under ASHRAE standards with EnergyPlus Weather (EPW) data. A genetic algorithm in Galapagos utilized to optimize façade openings, DSF cavity depth, and air-driven conduit dimensions to minimize EUI. Computational Fluid Dynamics (CFD) analysis in ANSYS FLUENT was conducted pre and post optimization to evaluate airflow behavior and validate ventilation efficiency. The optimized model achieved a 66 % reduction in EUI compared to the reference model, improved the Energy Performance Certificate (EPC) from G to C, and maintained Air Change per Hour (ACH) within ASHRAE standards. In conclusion, this study highlights the effectiveness of multi-variable optimization in improving both energy efficiency and ventilation performance. The integration of air-driven conduits into the DSF system, combined with a sequential optimization framework followed by post-optimization CFD validation, presents a streamlined approach to optimizing EUI and natural ventilation in high-rise buildings. Moreover, it provides valuable insights for early-stage high-rise building design, aiding in the reduction of reliance on mechanical systems and supporting sustainable building practices.