We propose an algorithm that combines proper orthogonal decomposition with a spectral method to analyze and extract reduced order models of flows from time data series of velocity fields. The flows considered in this study are assumed to be driven by non-linear dynamical systems exhibiting a complex behavior within quasiperiodic orbits in the phase space. The technique is appropriate to achieve efficient reduced order models even in complex cases for which the flow description requires a discretization with a fine 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 3,900.
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This research has been funded by Argentine and French governments through grants LIA FMF/PMF, Huracan, DIGITEO, PIP 3303, and UBACYT IN017.
Communicated by T.A. Zang.
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Cammilleri, A., Gueniat, F., Carlier, J. et al. POD-spectral decomposition for fluid flow analysis and model reduction. Theor. Comput. Fluid Dyn. 27, 787–815 (2013) doi:10.1007/s00162-013-0293-2
- Reduced order modeling
- Spectral analysis