%0 Journal Article %1 leclainche2017higher %A Le Clainche, Soledad %A Vega, José M. %D 2017 %I American Institute of Physics %J Physics of Fluids %K 37l30-infinite-dimensional-dissipative-dynamical-systems-attractors-and-their-dimensions-lyapunov-exponents 37n10-dynamical-systems-in-fluid-mechanics-oceanography-fluid-mechanics 76f20-dynamical-systems-approach-to-turbulence %N 8 %P 084102-- %R 10.1063/1.4997206 %T Higher order dynamic mode decomposition to identify and extrapolate flow patterns %U https://doi.org/10.1063/1.4997206 %V 29 %X This article shows the capability of using a higher order dynamic mode decomposition (HODMD) algorithm both to identify flow patterns and to extrapolate a transient solution to the attractor region. Numerical simulations are carried out for the three-dimensional flow around a circular cylinder, and both standard dynamic mode decomposition (DMD) and higher order DMD are applied to the non-converged solution. The good performance of HODMD is proved, showing that this method guesses the converged flow patterns from numerical simulations in the transitional region. The solution obtained can be extrapolated to the attractor region. This fact sheds light on the capability of finding real flow patterns in complex flows and, simultaneously, reducing the computational cost of the numerical simulations or the required quantity of data collected in experiments.

@article{leclainche2017higher, abstract = {This article shows the capability of using a higher order dynamic mode decomposition (HODMD) algorithm both to identify flow patterns and to extrapolate a transient solution to the attractor region. Numerical simulations are carried out for the three-dimensional flow around a circular cylinder, and both standard dynamic mode decomposition (DMD) and higher order DMD are applied to the non-converged solution. The good performance of HODMD is proved, showing that this method guesses the converged flow patterns from numerical simulations in the transitional region. The solution obtained can be extrapolated to the attractor region. This fact sheds light on the capability of finding real flow patterns in complex flows and, simultaneously, reducing the computational cost of the numerical simulations or the required quantity of data collected in experiments.}, added-at = {2020-09-03T07:46:47.000+0200}, author = {Le Clainche, Soledad and Vega, José M.}, biburl = {https://www.bibsonomy.org/bibtex/208d7ea36eb6b339d80288495ecebd8e5/gdmcbain}, comment = {doi: 10.1063/1.4997206}, doi = {10.1063/1.4997206}, interhash = {c28476d508daf4e06009a0a027fe4a28}, intrahash = {08d7ea36eb6b339d80288495ecebd8e5}, issn = {10706631}, journal = {Physics of Fluids}, keywords = {37l30-infinite-dimensional-dissipative-dynamical-systems-attractors-and-their-dimensions-lyapunov-exponents 37n10-dynamical-systems-in-fluid-mechanics-oceanography-fluid-mechanics 76f20-dynamical-systems-approach-to-turbulence}, month = aug, number = 8, pages = {084102--}, publisher = {American Institute of Physics}, timestamp = {2021-05-17T03:20:16.000+0200}, title = {Higher order dynamic mode decomposition to identify and extrapolate flow patterns}, url = {https://doi.org/10.1063/1.4997206}, volume = 29, year = 2017 }