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 co...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Wind |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2674-032X/3/3/22 |
| _version_ | 1797576350129717248 |
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| author | Thays G. A. Duarte Srinivasan Arunachalam Arthriya Subgranon Seymour M. J. Spence |
| author_facet | Thays G. A. Duarte Srinivasan Arunachalam Arthriya Subgranon Seymour M. J. Spence |
| author_sort | Thays G. A. Duarte |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T21:51:01Z |
| format | Article |
| id | doaj.art-dc101e0c20bb403c951eb5c8a61df7d1 |
| institution | Directory Open Access Journal |
| issn | 2674-032X |
| language | English |
| last_indexed | 2024-03-10T21:51:01Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Wind |
| spelling | doaj.art-dc101e0c20bb403c951eb5c8a61df7d12023-11-19T13:27:38ZengMDPI AGWind2674-032X2023-09-013337539310.3390/wind3030022Uncertainty Quantification and Simulation of Wind-Tunnel-Informed Stochastic Wind LoadsThays G. A. Duarte0Srinivasan Arunachalam1Arthriya Subgranon2Seymour M. J. Spence3Department of Civil and Coastal Engineering, University of Florida, Gainesville, FL 32611, USADepartment of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI 48109, USADepartment of Civil and Coastal Engineering, University of Florida, Gainesville, FL 32611, USADepartment of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI 48109, USAThe 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.https://www.mdpi.com/2674-032X/3/3/22stochastic wind load modelswind tunnel validationspectral representationproper orthogonal decompositionuncertainty quantificationshort-duration record |
| spellingShingle | Thays G. A. Duarte Srinivasan Arunachalam Arthriya Subgranon Seymour M. J. Spence Uncertainty Quantification and Simulation of Wind-Tunnel-Informed Stochastic Wind Loads Wind stochastic wind load models wind tunnel validation spectral representation proper orthogonal decomposition uncertainty quantification short-duration record |
| title | Uncertainty Quantification and Simulation of Wind-Tunnel-Informed Stochastic Wind Loads |
| title_full | Uncertainty Quantification and Simulation of Wind-Tunnel-Informed Stochastic Wind Loads |
| title_fullStr | Uncertainty Quantification and Simulation of Wind-Tunnel-Informed Stochastic Wind Loads |
| title_full_unstemmed | Uncertainty Quantification and Simulation of Wind-Tunnel-Informed Stochastic Wind Loads |
| title_short | Uncertainty Quantification and Simulation of Wind-Tunnel-Informed Stochastic Wind Loads |
| title_sort | uncertainty quantification and simulation of wind tunnel informed stochastic wind loads |
| topic | stochastic wind load models wind tunnel validation spectral representation proper orthogonal decomposition uncertainty quantification short-duration record |
| url | https://www.mdpi.com/2674-032X/3/3/22 |
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