NSF Award
2317176
This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). <br/><br/>This Engineering Research Initiation (ERI) award will focus on the characterization of near-surface wind fields to investigate the effectiveness of roof load mitigation strategies for low-rise buildings. Wind-induced damage to roof components of low-rise buildings is predominantly attributed to extreme suction loads caused by vortices that develop when the oncoming wind flow impinges on the structure, resulting in flow detachment near roof corners and edges. Previous research has demonstrated the effectiveness of several wind mitigation strategies for alleviating uplift roof pressures under a limited number of idealized wind tunnel flow conditions. This research will leverage a novel flow-control instrument at the University of Florida Natural Hazards Engineering Research Infrastructure (NHERI) Experimental Facility to physically simulate a wide range of atmospheric flows in a large boundary layer wind tunnel. The project will integrate machine learning and computational modeling to predict extreme wind loading for unexplored wind tunnel configurations and fill critical knowledge gaps associated with the complex relation between atmospheric turbulence and aerodynamic loading. The large volumes of velocity and pressure data will be made available in the NHERI Data Depot (https://www.DesignSafe-ci.org). The research will address fundamental fluid-structure interaction questions while enhancing the performance of roof systems, and ultimately contribute to increasing wind hazard resilience of low-rise buildings. This award will contribute to the National Science Foundation role in the National Windstorm Impact Reduction Program (NWIRP). <br/><br/>The specific goals of this research include: (i) the physical characterization of natural wind flows with precisely modulated frequency content, including low-frequency (large-scale) turbulence, which is traditionally deficient in wind tunnel tests conducted on large low-rise building models; (ii) experimental assessment of roof uplift load mitigation strategies for low-rise buildings tested under a wide range of properly calibrated upwind terrain conditions and large-scale turbulent structures; (iii) development and refinement of a deep neural network (DNN) to map complex relationships between incident turbulence and the effectiveness of wind mitigation strategies for reducing peak suction loads; and (iv) the calibration of numerical inflow turbulence models using high-fidelity flow velocity and pressure wind tunnel data. The datasets will offer a reliable calibration tool to enhance the accuracy of numerical models, and ultimately help advance computational wind engineering.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
