CFD and Deep Learning Based Natural Ventilation Analysis in Buildings

Natural ventilation is crucial for sustainable buildings and is also a promising solution for addressing indoor air quality (IAQ) issues such as those related to COVID-19. This thesis examines the efficacy of wind-driven cross-ventilation for a low-rise residential building with complex geometry and internal partitions typical of common constructions using computational fluid dynamics (CFD) simulations. Different wind speeds and directions with varying partition and window configurations are analyzed, as well as surrounding buildings consistent with Canadian suburban neighbourhoods. While CFD solvers are effective in predicting natural ventilation, they are limited by processing time, hardware and storage requirements, and specialized knowledge, which limits the number of designs tested and leads to suboptimal solutions. Therefore, user-friendly deep learning models are also developed to efficiently predict the velocity field within a cross-ventilated building, using both a Vanilla U‑Net and a U-Net with an attention mechanism for the neural network architectures. The models obtain training data from CFD simulations performed on a generic building with multiple opening sizes and impacts from different wind directions. The results show that partition walls block airflow and create dead zones, but when openings are introduced on partition walls to form a network of openings, IAQ is significantly improved, especially in rooms that previously only had a doorway opening. Additionally, surrounding buildings should not be neglected when accounting for IAQ, as the air changes per hour (ACH) can be reduced by more than half, leading to a significant increase in the local mean age of air (MAA) (up to 215%) for the entire building. Furthermore, both deep learning models generate velocity contours much faster than CFD solvers while only sacrificing a small amount of error. However, the Vanilla U-Net model is recommended as it had superior performance in both qualitative and quantitative analyses.