Abstract

Wind-Driven Rain (WDR) loading on building facades is a critical environmental factor. The adverse effects of WDR include material degradation, frost damage, salt efflorescence, and structural failures, requiring accurate WDR assessment methods for sustainable building design. This Ph.D. dissertation provides a comprehensive investigation into WDR loading through a combination of state-of-the-art reviews, Computational Fluid Dynamics (CFD) modeling, ISO semi-empirical model refinements, and Machine Learning (ML)-based approaches.
First, a systematic review of WDR studies, summarizing experimental, numerical, and semi-empirical methodologies while highlighting key influencing factors such as meteorological and geometrical parameters, and identifies limitations in current methodologies, particularly the ISO model's performance in urban settings.
The second part focuses on CFD modeling of WDR, using OpenFOAM, for a mid-rise residential building in Vancouver, Canada. Four different steady-state RANS models (i.e., standard k-ω, realizable k- ε, RNG k- ε, and standard k- ε) are compared and validated against wind-tunnel and on-site field measurements. The results indicate that the standard k-ω RANS model without incorporating turbulent dispersion provides slightly better performance and is therefore selected for subsequent analyses. Moreover, two WDR modeling techniques (i.e., Lagrangian Particle Tracking (LPT) and Eulerian Multiphase (EM)) are evaluated. Comparative analysis reveals that the RANS-EM provides more accurate predictions with lower computational costs, making it a preferable approach for urban WDR assessment.
The third part examines the impact of upstream buildings on WDR by modifying the Obstruction Factor within the ISO model. Significant discrepancies, up to a factor of five, are found between ISO predictions and modeled WDR by CFD. A refined Obstruction Factor is proposed to enhance the model’s accuracy in urban areas.
Finally, ML models are applied to further refine the ISO model. A CFD-generated dataset is used to train six different ML models (e.g., Artificial Neural Network (ANN)), resulting in an improved Wall Factor that accounts for a broader range of building geometries and meteorological conditions. Validated against field measurements, achieves up to 53% error reduction compared to the original ISO model.
This dissertation contributes to the development of climate-resilient building designs and offers practical improvements for WDR calculation on building facades in urban areas.