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This study investigates the complementary effects of side and corner modification strategies for the aerodynamic performance of tall buildings. A total of 81 doubly symmetric models were examined. High-frequency force balance (HFFB) wind tunnel testing was conducted at the University of Florida’s (UF) boundary layer wind tunnel (BLWT), an NSF-sponsored Natural Hazard Engineering Research Infrastructure (NHERI) Experimental Facility. The 81 models were examined under two approach flow conditions, which are suburban and open terrains. For each flow condition, the models were tested under 10 different wind angles from 0° to 45°. The base responses were recorded using a 6-axis load cell. A total of 1620 tests (81 models × 2 flow conditions × 10 wind angles) were performed in the BLWT at UF. Details are provided in the report document. 

The impact of climate change and global warming makes it imperative to seek sustainable solutions for the built environment. To facilitate the design of future sustainable buildings, wind tunnel tests are conducted in this study to investigate the flow characteristics and wind energy potential over a flat building roof with different edge configurations. Specifically, this study addresses the effect of parapet walls and roof edge-mounted solar panels on the wind flow over a flat-roof tall building. The results show that parapet walls generally slow down the wind speed and increase turbulence intensity as well as skewness angle, which compromises the efficiency of traditional turbine-based wind energy harvesting. On the other hand, the presence of solar panels on the roof edge (or on the top of the parapet wall) further alters flow separation and has the potential to enhance wind energy harvesting over the roof, especially for the solar panel inclined at 30°. In addition to providing valuable data for validating computational fluid dynamics (CFD) simulations, this study could also help to guide the design of wind energy harvesting devices on the building roof and explore the promising synergy with solar panels. 

Aerodynamic shape optimization is very useful for enhancing the performance of wind-sensitive structures. However, shape parameterization, as the first step in the pipeline of aerodynamic shape optimization, still heavily depends on empirical judgment. If not done properly, the resulting small design space may fail to cover many promising shapes, and hence hinder realizing the full potential of aerodynamic shape optimization. To this end, developing a novel shape parameterization scheme that can reflect real-world complexities while being simple enough for the subsequent optimization process is important. This study proposes a machine learning-based scheme that can automatically learn a low-dimensional latent representation of complex aerodynamic shapes for bluff-body wind-sensitive structures. The resulting latent representation (as design variables for aerodynamic shape optimization) is composed of both discrete and continuous variables, which are embedded in a hierarchy structure. In addition to being intuitive and interpretable, the mixed discrete and continuous variables with the hierarchy structure allow stakeholders to narrow the search space selectively based on their interests. As a proof-of-concept study, shape parameterization examples of tall building cross sections are used to demonstrate the promising features of the proposed scheme and guide future investigations on data-driven parameterization for aerodynamic shape optimization of wind-sensitive structures. 

Aerodynamic shape optimization is very useful for enhancing the performance of wind-sensitive structures. However, shape parameterization, as the first step in the pipeline of aerodynamic shape optimization, still heavily depends on empirical judgment. If not done properly, the resulting small design space may fail to cover many promising shapes, and hence hinder realizing the full potential of aerodynamic shape optimization. To this end, developing a novel shape parameterization scheme that can reflect real-world complexities while being simple enough for the subsequent optimization process is important. This study proposes a machine learning-based scheme that can automatically learn a low-dimensional latent representation of complex aerodynamic shapes for bluff-body wind-sensitive structures. The resulting latent representation (as design variables for aerodynamic shape optimization) is composed of both discrete and continuous variables, which are embedded in a hierarchy structure. In addition to being intuitive and interpretable, the mixed discrete and continuous variables with the hierarchy structure allow stakeholders to narrow the search space selectively based on their interests. As a proof-of-concept study, shape parameterization examples of tall building cross sections are used to demonstrate the promising features of the proposed scheme and guide future investigations on data-driven parameterization for aerodynamic shape optimization of wind-sensitive structures