Computer Simulation of Stochastic Wind Velocity Fields for Structural Response Analysis: Comparisons and Applications
The digital simulation of wind velocity fields, modeled as multivariate stationary Gaussian processes, is a widely adopted tool to generate the external input for response analysis of wind-sensitive nonlinear structures. The problem does not entail any theoretical difficulty, existing already a larg...
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| Language: | English |
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Wiley
2010-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2010/749578 |
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| author | Filippo Ubertini Fabio Giuliano |
| author_facet | Filippo Ubertini Fabio Giuliano |
| author_sort | Filippo Ubertini |
| collection | DOAJ |
| description | The digital simulation of wind velocity fields, modeled as multivariate stationary Gaussian processes, is a widely adopted tool to generate the external input for response analysis of wind-sensitive nonlinear structures. The problem does not entail any theoretical difficulty, existing already a large number of well-established techniques, such as the accurate weighted amplitude wave superposition (WAWS) method. However, reducing the computational effort required by the WAWS method is sometimes necessary, especially when dealing with complex structures and high-dimensional simulation domains. In these cases, approximate formulas must be adopted, which however require an appropriate tuning of some fundamental parameters in such a way to achieve an acceptable level of accuracy if compared to that obtained using the WAWS method. Among the different techniques available for this purpose, autoregressive (AR) filters and algorithms exploiting the proper orthogonal decomposition (POD) of the spectral matrix deserve a special attention. In this paper, a properly organized way for implementing stochastic wind simulation algorithms is outlined at first. Then, taking the WAWS method as a reference from the viewpoint of the accuracy of the simulated samples, a comparative study between POD-based and AR techniques is proposed, with a particular attention to computational effort and memory requirements. |
| format | Article |
| id | doaj-art-b2c00cb7bb794af2af87d815c7c81c36 |
| institution | OA Journals |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2010-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-b2c00cb7bb794af2af87d815c7c81c362025-08-20T02:23:27ZengWileyAdvances in Civil Engineering1687-80861687-80942010-01-01201010.1155/2010/749578749578Computer Simulation of Stochastic Wind Velocity Fields for Structural Response Analysis: Comparisons and ApplicationsFilippo Ubertini0Fabio Giuliano1Department of Civil and Environmental Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia, ItalyDepartment of Structural and Geotechnical Engineering, University of Rome “La Sapienza”, Via Eudossiana 18, 00184 Rome, ItalyThe digital simulation of wind velocity fields, modeled as multivariate stationary Gaussian processes, is a widely adopted tool to generate the external input for response analysis of wind-sensitive nonlinear structures. The problem does not entail any theoretical difficulty, existing already a large number of well-established techniques, such as the accurate weighted amplitude wave superposition (WAWS) method. However, reducing the computational effort required by the WAWS method is sometimes necessary, especially when dealing with complex structures and high-dimensional simulation domains. In these cases, approximate formulas must be adopted, which however require an appropriate tuning of some fundamental parameters in such a way to achieve an acceptable level of accuracy if compared to that obtained using the WAWS method. Among the different techniques available for this purpose, autoregressive (AR) filters and algorithms exploiting the proper orthogonal decomposition (POD) of the spectral matrix deserve a special attention. In this paper, a properly organized way for implementing stochastic wind simulation algorithms is outlined at first. Then, taking the WAWS method as a reference from the viewpoint of the accuracy of the simulated samples, a comparative study between POD-based and AR techniques is proposed, with a particular attention to computational effort and memory requirements.http://dx.doi.org/10.1155/2010/749578 |
| spellingShingle | Filippo Ubertini Fabio Giuliano Computer Simulation of Stochastic Wind Velocity Fields for Structural Response Analysis: Comparisons and Applications Advances in Civil Engineering |
| title | Computer Simulation of Stochastic Wind Velocity Fields for Structural Response Analysis: Comparisons and Applications |
| title_full | Computer Simulation of Stochastic Wind Velocity Fields for Structural Response Analysis: Comparisons and Applications |
| title_fullStr | Computer Simulation of Stochastic Wind Velocity Fields for Structural Response Analysis: Comparisons and Applications |
| title_full_unstemmed | Computer Simulation of Stochastic Wind Velocity Fields for Structural Response Analysis: Comparisons and Applications |
| title_short | Computer Simulation of Stochastic Wind Velocity Fields for Structural Response Analysis: Comparisons and Applications |
| title_sort | computer simulation of stochastic wind velocity fields for structural response analysis comparisons and applications |
| url | http://dx.doi.org/10.1155/2010/749578 |
| work_keys_str_mv | AT filippoubertini computersimulationofstochasticwindvelocityfieldsforstructuralresponseanalysiscomparisonsandapplications AT fabiogiuliano computersimulationofstochasticwindvelocityfieldsforstructuralresponseanalysiscomparisonsandapplications |