WING WALL OPTIMIZATION
Research details.
The recent global pandemic has shown the importance of natural ventilation not only in reducing energy consumption but also in reducing airborne contamination. Long-established studies have demonstrated the advantages of window wing walls – vertical projections attached to windows that create a pressure change near the openings - to increase indoor wind flow. However, these conventional strategies to improve natural ventilation rarely take into account site-specific conditions such as dynamic wind conditions throughout the year. This research aims to find a design configuration for window wing walls to improve air circulation through careful interventions for the whole year in classrooms with conventional one-sided openings. With data-driven design, natural ventilation can be maximized to reduce the risk of contamination by insufficient fresh air.
The paper utilizes the Computational Fluid Dynamics (CFD) simulations and Artificial Neural Networks (ANN) to predict indoor air movement with less computational time and load. Coupled with Genetic Algorithm (GA), the paper develops an approach that would enable increased natural ventilation in rooms with one-sided windows.
Team: Yun Kyu Yi, Manal Anis, Keun Jang, You-Jeong Kim
Publication
Application of machine learning (ML) and genetic algorithm (GA) to optimize window wing wall design for natural ventilation. Journal of Building Engineering, 68, 106218.
