Extracting Stochastic Airwake Models from a Database for Engineering Analysis and Simulation
Abstract:A framework is presented for extracting interpretive models of airwake autocorrelation and autospectrum as well as crosscorrelation and cross-spectrum from a database. These models have a simple analytical structure that aids routine simulation and application as a predictive tool. Airwake refers to turbulence shed from the ship superstructure, and the database, to a set of spectral (autospectral and cross-spectral) points of flow velocity data from experimental and computational fluid dynamics–based investigations. The framework is developed from first principles: It is based on perturbation theory; it addresses all three velocity components, and it is tested against a comprehensive database under different superstructure and wind-over-deck conditions. For each velocity component, the autocorrelation and cross-correlation are represented by separate perturbation series in which the first terms have a form of the von Karman longitudinal or lateral correlation function. These series are then transformed into equivalent perturbation series of autospectra and cross-spectra. The perturbation coefficients are evaluated by satisfying the algorithmic constraints and fitting a curve on a set of selected spectral data points in the low-frequency bandwidth (0≤f(Hz)≤1.6); the emphasis is on extracting spectral models for this bandwidth. Generally, no more than a second-order perturbation correction (a three-term perturbation series) is necessary, and the extracted models lend themselves well to construction of shaping filters driven by white noise. The framework's strengths and weaknesses are discussed as well.
Document Type: Research Article
Publication date: 2012-04-01
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