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This project has investigated the wind-induced pressure loads on a realistic low-rise building within an urban area (Y2E2 building in Stanford Engineering Quad). Experiments were conducted at two wind tunnels; the NHERI Wall of Wind (WOW) wind tunnel at the Florida International University facility, and the NHERI terraformer Boundary Layer Wind Tunnel at the University of Florida (UF). First, the building is considered as isolated with suburban terrain in both WT facilities. Consequently, the surrounding buildings were included onto the turntable, and pressure measurements were conducted on the target building. In all experiments, the measurements were performed across a total of 28 wind directions, encompassing the complete wind rose.
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Uncertainty Quantification and Simulation of Wind-Tunnel-Informed Stochastic Wind Loads
The simulation of stochastic wind loads is necessary for many applications in wind engineering. The proper-orthogonal-decomposition-(POD)-based spectral representation method is a popular approach used for this purpose, due to its computational efficiency. For general wind directions and building configurations, the data-informed POD-based stochastic model is an alternative that uses wind-tunnel-smoothed auto- and cross-spectral density as input, to calibrate the eigenvalues and eigenvectors of the target load process. Even though this method is straightforward and presents advantages, compared to using empirical target auto- and cross-spectral density, the limitations and errors associated with this model have not been investigated. To this end, an extensive experimental study on a rectangular building model considering multiple wind directions and configurations was conducted, to allow the quantification of uncertainty related to the use of short-duration wind tunnel records for calibration and validation of the data-informed POD-based stochastic model. The results demonstrate that the data-informed model can efficiently simulate stochastic wind loads with negligible model errors, while the errors associated with calibration to short-duration wind tunnel data can be important.
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- PAR ID:
- 10538325
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Wind
- Volume:
- 3
- Issue:
- 3
- ISSN:
- 2674-032X
- Page Range / eLocation ID:
- 375 to 393
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/wind3030022
