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

Mohammad N. Murshed; M. Monir Uddin; IEEE

doi:10.1109/ICCIT51783.2020.9392659

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

Mohammad N. Murshed, M. Monir Uddin and IEEE

2020 23rd International Conference on Computer and Information Technology (ICCIT), pp.1-4

19/12/2020

DOI: https://doi.org/10.1109/ICCIT51783.2020.9392659

Abstract

Data-driven modeling

discrete Fourier transform

Discrete Fourier transforms

dynamic mode decomposition

Information filters

Information technology

singular values

spectrum

Tools

turbulent channel flow

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.

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Title: Low rank approximation of the turbulent channel flow by filter-reinforced Dynamic Mode Decomposition
Creators - without role: Mohammad N. Murshed - North South University,Department of Mathematics & Physics,Dhaka,Bangladesh
M. Monir Uddin - North South University,Department of Mathematics & Physics,Dhaka,Bangladesh
IEEE
Publication Details: 2020 23rd International Conference on Computer and Information Technology (ICCIT), pp.1-4
Publisher: IEEE
Identifiers: 9923241808331
Academic Unit: King Khalid University
Language: English
Resource Type: Conference proceeding