Model reduction using Dynamic Mode Decomposition

Abstract

Dynamic Mode Decomposition (DMD) is a recent post-processing technique that extracts from snapshots dynamic relevant information for the flow. Without explicit knowledge of the dynamical operator, the DMD algorithm determines eigenvalues and eigenvectors of an approximate linear model. The ability of DMD to extract dynamically relevant features of the flow predispose it for building a representative reduced-order subspace from the data and for deriving a reduced-order model. The use of the DMD for reduced-order modelling will be investigated in this paper on experimental flow data of a cylinder wake.