The numerical simulation of Gaussian cross-correlated wind velocity fluctuations by means of a hybrid model

Abstract

Abstract In the present work, a hybrid model is employed to simulate cross-correlated wind velocity components. The model is based on the theory of expansions in convergent series of proper functions of the realizations of the target process, which is the base of parametric models, but the coefficients of the expansion are generated by means of an extended AR filter. The coefficients of the AR filter can be evaluated directly from the spectral density tensor of the target process, without making use of Yule-Walker equations and, consequently, without the integration of the spectral density tensor to find the covariance tensor. In this way, the flexibility of parametric models is retained together with the main characteristics of ARIMA digital filters, which need a relatively small amount of memory in order to generate all the coefficients needed. The goodness of the results is investigated in the frequency domain by means of the comparison between the target and estimated auto- and cross-power spectral densities.