DOI: 10.17603/ds2-e7qm-ef64
title: Pressure coefficients on a simple geometry Bluff body generated by a randomized terrain in a Boundary Layer Wind Tunnel
title: in Modeling of Higher-Order Turbulence from Randomize Terrain in a Boundary Layer Wind Tunnel

description: In the first phase of this project (Ojeda et al., 2023), turbulence profiles were collected for various Terraformer roughness element configurations, generating a comprehensive dataset on boundary layer flow as a function of upwind fetch. Based on these profiles, select configurations 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 each Terraformer configuration: lateral approach flow profiles, reference wind velocity, and pressure coefficients on the bluff body.
The content of this dataset consists in the bluff body model exposed to a subset of 5 Benchmark repetitions and 22 different Terraformer configurations from the first part of this project (Ojeda et al., 2023). Each Benchmark repetition includes wind directions ranging from 0° to 180° in 15° increments (13 directions total), while each Phase 1 repetition includes wind directions from 0° to 90° in 15° increments (7 directions total).
This dataset provides valuable insights into the influence of upwind fetch and surface roughness on wind-induced forces, enabling a deeper understanding of how different terrain conditions affect boundary layer characteristics. By analyzing variations in approach flow turbulence and pressure distributions on the bluff body, researchers can refine aerodynamic models and improve the accuracy of wind load predictions on structures. These findings contribute to the development of more effective design strategies for buildings, bridges, and other infrastructure exposed to wind forces, ultimately enhancing their resilience and safety.

description: 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 (Wind Profiles in a Boundary Layer Wind Tunnel based on Different Approach Terrain Configurations) 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 (Pressure coefficients on a simple geometry Bluff body generated by a randomized terrain in a Boundary Layer Wind Tunnel) 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.