A new simulation algorithm of multivariate short-term stochastic wind velocity field based on inverse fast Fourier transform

•The presented formula is theoretically proved and firstly adopted to simulate wind.•The discrete frequency is started from 1 for ergodicity, zero frequency is excluded.•The ergodicity of the cross-correlation function is carefully investigated.•The period of the any simulated process is exactly equal to the length of the sample.•The computing efficiency is increased four times, specially more than one thousand times.

A novel algorithm to generate samples of stationary multivariate stochastic processes is presented according to a Fourier–Stieltjes integral and based on a strict mathematical deduction. The algorithm is very efficient as it takes full advantage of inverse fast Fourier transform technique, and is especially applicable to the simulation of short-term wind velocity field with many simulation points. By creation of a conjugate-symmetric series, the summing operation of the decomposed lagged coherence matrix is conducted in the frequency-domain, which decreased the executions of IFFT. Furthermore, the ergodic property of the algorithm is carefully investigated, and computational efficiency is compared with that of the existing method.