Aims
This study develops a systematic framework to identify, classify, and analyze the key determinants of natural ventilation in double-skin façade (DSF) systems using a scoping review based on the Arksey and O'Malley framework. It focuses on three dimensions: environmental-climatic indicators, geometric-physical parameters, and simulation-based assessment methodologies. The goal is to map existing knowledge, reduce literature fragmentation, and establish a foundation for climate-responsive DSF design to enhance thermal comfort and reduce mechanical ventilation reliance.
Materials & Methods
Using the five-stage Arksey and O'Malley framework, a systematic search was conducted across Scopus, Web of Science, ScienceDirect, and Magiran databases for the years 2018–2025 using the keywords "Double-skin façade" and "Natural ventilation". Out of 1,024 initial sources, 39 original, full-text English or Persian articles were selected based on specific inclusion criteria. Data extraction utilized charting and directed content analysis, with validity maintained through concurrent screening by two independent researchers.
Findings
The analysis of the 39 studies reveals that DSF natural ventilation performance depends on the interaction of three categories:
• Environmental parameters: Air temperature, wind speed/direction, solar radiation, and relative humidity, alongside shading systems and opening configurations.
• Geometric characteristics: Cavity depth (depths of 0.2 to 1.0 m enhance the stack effect, while excessive depths reduce efficiency), opening size/location, glazing type, shading configuration, and façade typology.
• Evaluation methods: CFD tools (e.g., ANSYS Fluent, SolidWorks Flow) for airflow/thermal analysis; Building Energy Simulation (BES) software (e.g., EnergyPlus, DesignBuilder, TRNSYS) for energy/comfort; and parametric platforms (Grasshopper with Ladybug/Honeybee) for early-stage optimization.
Conclusion
The scoping review of 39 articles (2018–2025) indicates that DSF performance is driven by the interaction of environmental and geometric factors, requiring an integrated, performance-based design approach. A key gap exists between simulation predictions and actual performance due to limited experimental data, methodological inconsistencies, and lack of standardized criteria. Future research should integrate field measurements with simulations, develop experimental databases and guidelines, and employ AI/machine learning for multi-variable optimization