POD-Spectral Decomposition for Fluid Flow Analysis and Model Reduction

Abstract : We propose an algorithm that combines Proper Orthogonal Decomposition with a spectral method to analyse and extract from time data series of velocity fi elds, reduced order models of flows. The flows considered in this study are assumed to be driven by non linear dynamical systems exhibiting a complex behavior within quasi-periodic orbits in the phase space. The technique is appropiate to achieve efficient reduced order models even in complex cases for which the flow description requires a discretization with a ne spatial and temporal resolution. The proposed analysis enables to decompose complex flow dynamics into modes oscillating at a single frequency. These modes are associated with different energy levels and spatial structures. The approach is illustrated using time resolved PIV data of a cylinder wake flow with associated Reynolds number equal to 3900.
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Theoretical and Computational Fluid Dynamics, Springer Verlag, 2013, 27 (125), 〈10.1007/s00162-013-0293-2〉
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Ada Cammilleri, Florimond Guéniat, Johan Carlier, Luc Pastur, Etienne Mémin, et al.. POD-Spectral Decomposition for Fluid Flow Analysis and Model Reduction. Theoretical and Computational Fluid Dynamics, Springer Verlag, 2013, 27 (125), 〈10.1007/s00162-013-0293-2〉. 〈hal-00793380〉

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