SIMULATION OF ERGODIC MULTIVARIATE STOCHASTIC PROCESSES

The authors of this technical paper propose a simulation algorithm to generate sample functions of a stationary, multivariate stochastic process according to its prescribed cross-spectral density matrix. The process is nonhomogeneous in space if the components of the vector process correspond to different locations in space. The ensemble cross-correlation matrix of the generated sample functions is identical to the corresponding target. The simulation algorithm generates ergodic sample functions in the sense that the temporal cross-correlation matrix of generated sample functions is identical to the corresponding target, when the length of the generated sample function is equal to one period. The proposed algorithm is computationally efficient, taking advantage of the fast Fourier transform technique, and is based on an extension of the spectral representation technique. The generated sample functions are Gaussian in the limit as the number of terms in the frequency discretization of the cross-spectral density matrix approaches infinity. The authors present an example involving simulation of turbulent wind velocity fluctuations to demonstrate the capabilities and efficiency of the proposed algorithm.

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  • English

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  • Accession Number: 00728787
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Nov 30 1996 12:00AM