Application of the dynamic mode decomposition to experimental data
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The dynamic mode decomposition (DMD) is a data-decomposition technique that allows the extraction of dynamically relevant flow features from time-resolved experimental (or numerical) data. It is based on a sequence of snapshots from measurements that are subsequently processed by an iterative Krylov technique. The eigenvalues and eigenvectors of a low-dimensional representation of an approximate inter-snapshot map then produce flow information that describes the dynamic processes contained in the data sequence. This decomposition technique applies equally to particle-image velocimetry data and image-based flow visualizations and is demonstrated on data from a numerical simulation of a flame based on a variable-density jet and on experimental data from a laminar axisymmetric water jet. In both cases, the dominant frequencies are detected and the associated spatial structures are identified.
KeywordsShear Layer Proper Orthogonal Decomposition Proper Orthogonal Decomposition Mode Dynamic Mode Decomposition Arnoldi Method
Support from the Agence Nationale de la Recherche (ANR) through their “chaires d’excellence” program and from the Alexander-von-Humboldt Foundation is gratefully acknowledged.
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