Yousun Li

TL;DR: In this paper, the fast Fourier transform (FFT) technique is utilized to simulate a multivariate nonstationary Gaussian random process with prescribed evolutionary spectral description, and a stochastic decomposition technique facilitates utilization of the FFT algorithm.

TL;DR: A numerical simulation technique is presented that combines the advantages of the discrete Fourier transform (DFT) algorithm and a digital filtering scheme to generate continuous long-duration multivariate random processes.

TL;DR: In this paper, an autoregressive and moving average (ARMA) recursive model is used to simulate the time series of wind loads, and an ARMA algorithm based on a three-stage matching method and a scheme which combines ARMA and digital interpolation filters are presented to efficiently simulate realizations of wind loading at the prescribed time increments.

TL;DR: In this article, a numerical simulation scheme is presented that combines the advantages of the discrete Fourier transform algorithm and a digital filtering scheme to generate nonstationary multivariate random processes.

TL;DR: A stochastic decomposition technique is developed that improves the efficiency of conventional frequency-domain analysis by eliminating the intermediate step of estimating cross-spectral density matrices.