Low rank approximation of the turbulent channel flow by filter-reinforced Dynamic Mode Decomposition

Dynamic Mode Decomposition (DMD) is a data-driven modeling tool that can create a model from time-series data of a quantity of interest in a particular problem. We propose a new version of DMD to efficiently identify the modes in the complex turbulent channel flow. This modified version employs Discrete Fourier Transform to remove the low-amplitude high-frequency content in the images and results in a spectrum that shows the most dominant modes that contribute to the evolution of the flow.