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Dynamic Mode Decomposition (DMD) is a useful tool to effectively extract thedominant dynamic flow structure from a unsteady flow field. However, DMDrequires massive computational resources with respect to memory consumption andthe usage of storage. In this paper, an alternative incremental algorithm ofTotal DMD (Incremental TDMD) is proposed which is based on Incremental SingularValue Decomposition (SVD). The advantage of Incremental TDMD compared to theexisting on-the-fly algorithms of DMD is that Sparsity-Promoting DMD (SPDMD)can be performed after the incremental process without saving huge datasets onthe disk space. SPDMD combined with Incremental TDMD enable the effectiveidentification of dominant modes which are relevant to the results fromconventional TDMD combined with SPDMD.

On-the-fly algorithm for Dynamic Mode Decomposition using Incremental Singular Value Decomposition and Total Least Squares