This paper presents the ongoing research toward developing a stochastically complete airwake model from the experimental and CFD-based results (baseline data); stochastically complete means autospectra (autospectral densities) of all velocity components and cross-spectra (cross-spectral densities) of any combination of velocity components at two arbitrarily chosen points. As for the approach, the autocorrelations and cross-correlations in the time domain are approximated by perturbation series, in which the first terms have a form of the corresponding von Karman point correlation and two-point cross-correlation functions. These series are transformed into equivalent perturbation series of autospectra and cross-spectra in the frequency domain, and then the basic characteristics of the baseline data are matched with those of the approximated in the frequency domain. Simply put, an interpretive model is extracted from the baseline data for routine applications such as a predictive tool and for qualitatively describing airwake turbulence in both domains. The emphasis, throughout, is on cross-spectra; in particular, the earlier-proposed perturbation scheme for autospectra is generalized to model cross-spectra. The assumption is that the magnitude of co-spectrum (real part of cross-spectrum) far exceeds that of quad-spectrum (imaginary part) in the low-frequency region (say 0.06 < f Hz < 1.6), and its strengths and weaknesses are included as well.
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