Filter approach to the stochastic analysis of MDOF wind-excited structures

Salvatore Benfratello, Salvatore Benfratello, Giuseppe Muscolino, Giuseppe Alfredo Muscolino

Risultato della ricerca: Articlepeer review

14 Citazioni (Scopus)

Abstract

In this paper, an approach useful for stochastic analysis of the Gaussian and non-Gaussian behavior of the response of multi-degree-of-freedom (MDOF) wind-excited structures is presented. This approach is based on a particular model of the multivariate stochastic wind field based upon a particular diagonalization of the power spectral density (PSD) matrix of the fluctuating part of wind velocity. This diagonalization is performed in the space of eigenvectors and eigenvalues that are called here wind-eigenvalues and wind-eigenvectors, respectively. From the examination of these quantities it can be recognized that the wind-eigenvectors change slowly with frequency while the first wind-eigenvalue dominates all the others in the low-frequency range. It is shown that the wind field can be modeled in a satisfactory way by taking the first wind-eigenvector as constant and by retaining only the first eigenvalue in the calculations. The described model is then used for stochastic analysis in the time domain of MDOF wind-excited structures. This is accomplished by modeling each element of the diagonalized wind-PSD matrix as the velocity PSD function of a set of second-order digital filters with viscous damping driven by white noise of selected intensity. This approach makes it easy to obtain in closed form the statistical moments of every order of the structural response, taking full advantage of the Ito calculus. Moreover, in the proposed approach, it is possible to reduce the computational effort by appropriately selecting the number of wind modes retained in the calculation.
Lingua originaleEnglish
pagine (da-a)311-321
Numero di pagine11
RivistaProbabilistic Engineering Mechanics
Volume14
Stato di pubblicazionePublished - 1999

All Science Journal Classification (ASJC) codes

  • Statistical and Nonlinear Physics
  • Civil and Structural Engineering
  • Nuclear Energy and Engineering
  • Condensed Matter Physics
  • Aerospace Engineering
  • Ocean Engineering
  • Mechanical Engineering

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