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Boundary Layer Wind Tunnel (BLWT) facilities are commonly used for assessing wind loads on structures. Although BLWT facilities routinely match 1st and 2nd-order wind profile models, evidence suggests that turbulence in the roughness sublayer and the inertial sublayer exhibit non-Gaussian higher-order properties. These non-Gaussian properties can influence peak wind pressures, which govern certain structural limit states and play an important role in design. In the first part of this project, Machine learning (ML) methods are employed to identify relationships between roughness element configurations and higher-order statistical properties of the wind field. A semi-automated framework with an active learning portion and a wind tunnel experimental procedure is developed. The learning framework adaptively selects roughness profiles and launches new experiments to identify differing profiles with second-order equivalent flow as quantified by turbulence intensity. The premise is that second-order equivalent wind fields can differ in higher-order properties and therefore extreme value derived peak loads may differ. Over the course of this project, the turbulence profiles from hundreds of different Terraformer roughness element configurations were collected, providing a very rich dataset of boundary layer flow as a function of upwind fetch. Experiment 1 provides the metadata to describe and interpret measured wind profiles at the UFBLWT for a data set collected for the Benchmark experiments and 3 different phases: 1) Sinusoidal waves experiments, 2) Shape study experiments and, 3) Random field experiments. Experiment 2 of this dataset presents the results of experiments conducted in the UFBLWT, with a focus on measuring turbulence characteristics and pressure coefficients on a bluff body under varying terrain roughness configurations. The dataset provides valuable insights into the influence of upwind fetch and surface roughness on wind-induced forces, contributing to improved modeling and prediction of wind loads on structures. Based on the Terraformer configurations in experiment 1, select configurations (Benchmark and Phase 1 Terraformer configurations only) were chosen for bluff body experiments, along with additional approach turbulence measurements at a lateral location to the model. This dataset includes three key components for Benchmark and Phase 1 Terraformer configurations: reference wind velocity (uRef), lateral approach flow profiles (LatFlow), and pressure coefficients (Cpdata) on the bluff body. 

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.