POD-spectral decomposition for fluid flow analysis and model reduction

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...

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Bibliographic Details
Published in:Theoretical and computational fluid dynamics 2013-11, Vol.27 (6), p.787-815
Main Authors: Cammilleri, A., Gueniat, F., Carlier, J., Pastur, L., Memin, E., Lusseyran, F., Artana, G.
Format: Article
Language:English
Subjects:
Algorithms
Classical and Continuum Physics
Computational fluid dynamics
Computational Science and Engineering
Cylinders
Decomposition
Decomposition (Mathematics)
Discretization
Dynamical systems
Engineering
Engineering Fluid Dynamics
Engineering Sciences
Fluid dynamics
Fluid flow
Mathematical physics
Original Article
Reactive fluid environment
Reduced order models
Simulation
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Summary: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.
ISSN:0935-4964
1432-2250
DOI:10.1007/s00162-013-0293-2