Application of the dynamic mode decomposition to experimental data

Peter J. Schmid 1, *
Abstract : 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.
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Experiments in Fluids, Springer Verlag (Germany), 2011, 50 (4), pp.1123-1130. 〈10.1007/s00348-010-0911-3〉
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Soumis le : vendredi 18 juillet 2014 - 10:15:18
Dernière modification le : jeudi 10 mai 2018 - 02:03:38

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Peter J. Schmid. Application of the dynamic mode decomposition to experimental data. Experiments in Fluids, Springer Verlag (Germany), 2011, 50 (4), pp.1123-1130. 〈10.1007/s00348-010-0911-3〉. 〈hal-01025583〉

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