Extracting Airwake Cross-Correlation and Cross-Spectrum Models from a Database for Routine Applications

A framework based on perturbation theory is presented for extracting crosscorrelation and cross-spectrum models from a database from experimental and CFD-based investigations. This framework covers all three velocity components at two arbitrarily chosen points on a horizontal plane and for arbitrary orientation of relative wind. These extracted models are interpretive models in that they have a simpler analytical structure that helps delineate airwake in both domains; moreover, they are virtually tailored to routine simulations and such other applications, say as a predictive tool. As for the approach, the crosscorrelations are approximated by perturbation series, in which the first terms have a form of the corresponding von Karman two-point correlation functions. These series are then transformed into equivalent perturbation series of cross-spectra. The perturbation coefficients are determined by satisfying the analytical constraints in the formulation and through a nonlinear curve-fitting on a set of selected data points in the low-frequency bandwidth (0.06 ≤ f Hz ≤ 1.6). The strengths and weaknesses of the framework and its applications to a database are included; generally, a first order perturbation correction (a two-term perturbation series) is found to be adequate.