A dynamic mode decomposition technique for the analysis of non–uniformly sampled flow data

• Published in: Journal of computational physics Vol. 468; p. 111495
• Main Authors: Li, Binghua, Garicano-Mena, Jesús, Valero, Eusebio
• Format: Journal Article
• Language: English
• Published: Cambridge Elsevier Inc 01.11.2022; Elsevier Science Ltd
• Subjects: Channel flow; Computational physics; Datasets; Decomposition; Dynamic mode decomposition; Feature detection; Missing data; Modal decompositions; Non–uniformly sampled datasets; Sampled data; Time integration; Modal decompositions; Feature detection; Dynamic mode decomposition; Non–uniformly sampled datasets
• ISSN: 0021-9991; 1090-2716
• DOI: 10.1016/j.jcp.2022.111495

A novel Dynamic Mode Decomposition (DMD) technique capable of handling non–uniformly sampled data is proposed. As it is usual in DMD analysis, a linear relationship between consecutive snapshots is made. The performance of the new method, which we term θ-DMD, is assessed on three different, increasingly complex datasets: a synthetic flow field, a ReD=60 flow around a cylinder cross section, and a Reτ=200 turbulent channel flow. For the three datasets considered, whenever the dataset is uniformly sampled, the θ-DMD method provides comparable results to the original DMD method. Additionally, the θ-DMD is still capable of recovering relevant flow features from non–uniformly sampled databases, whereas DMD cannot. The proposed tool opens the way to conduct DMD analyses for non–uniformly sampled data, and can be useful e.g., when confronted with experimental datasets with missing data, or when facing numerical datasets generated using adaptive time-integration schemes. •The θ-DMD method is a novel technique enabling to conduct DMD analyses of non–uniformly sampled data sequences.•θ-DMD applied to uniformly sampled data sets provides comparable results to traditional methods.•θ-DMD applied to non–equiseparated data sequences successfully retrieve physically meaningful modes.•θ-DMD can handle (possibly gappy) experimental and/or numerical datasets generated by adaptive integration schemes.