• Wind and Structures
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• 제16권5호
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• pp.517-540
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• 2013

This paper presents applications of proper orthogonal decomposition in both the time and frequency domains based on both cross spectral matrix and covariance matrix branches to analyze multi-variate unsteady pressure fields on prisms and to study spanwise and chordwise pressure distribution. Furthermore, modification of proper orthogonal decomposition is applied to a rectangular spanwise coherence matrix in order to investigate the spanwise correlation and coherence of the unsteady pressure fields. The unsteady pressure fields have been directly measured in wind tunnel tests on some typical prisms with slenderness ratios B/D=1, B/D=1 with a splitter plate in the wake, and B/D=5. Significance and contribution of the first covariance mode associated with the first principal coordinates as well as those of the first spectral eigenvalue and associated spectral mode are clarified by synthesis of the unsteady pressure fields and identification of intrinsic events inside the unsteady pressure fields. Spanwise coherence of the unsteady pressure fields has been mapped the first time ever for better understanding of their intrinsic characteristics.

• Wind and Structures
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• 제7권5호
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• pp.333-344
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• 2004

This paper illustrates application of the proper orthogonal decomposition (POD) and the autoregressive (AR) model to simulate large wind pressures due to gusts on a low-rise building. In the POD analysis, the covariance of the ensemble of large wind pressures is employed to calculate the principal modes and coordinates. The POD principal coordinates are modeled using the AR process, and the fitted AR models are employed to generate the principal coordinates. The generated principal coordinates are then used to simulate large wind pressures. The results show that the structure characterizing large wind pressures is well represented by the dominant eigenmodes (up to the first fifteen eigenmodes). Also, wind pressures with large peak values are simulated very well using the dominant eigenmodes along with the principal coordinates generated by the AR models.