BibTex format
@inproceedings{Beit-Sadi:2019,
author = {Beit-Sadi, M and Krol, J and Wynn, A},
title = {Data driven feature identification and sparse representation of turbulent flows},
url = {http://hdl.handle.net/10044/1/73152},
year = {2019}
}
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RIS format (EndNote, RefMan)
TY - CPAPER
AB - dentifying coherent structures of fluid flows is of greatimportance for reduced order modelling and flow control.Finding such structures in a turbulent flow, however, canbe challenging. A number of modal decomposition algo-rithms have been proposed in recent years which decom-pose snapshots of data into spatial modes, each associatedwith a single frequency and growth-rate, most prominentlydynamic mode decomposition (DMD). However, the num-ber of modes that DMD-like algorithms construct may beunrelated to the number of significant degrees of freedomof the underlying system. This provides a difficulty if onewants to create a low-order model of a flow. In this work,we present a method of post-processing DMD modes forextracting a small number of dynamically relevant modes.This is achieved by first ranking the DMD modes, then us-ing an iterative approach based on the graph-theoretic no-tion of maximal cliques to identify clusters of modes and,finally, by replacing each cluster with a single (pair of)modes.
AU - Beit-Sadi,M
AU - Krol,J
AU - Wynn,A
PY - 2019///
TI - Data driven feature identification and sparse representation of turbulent flows
UR - http://hdl.handle.net/10044/1/73152
ER -