Abstract
Abstract
Highlights
Analysis of turbulence remains as one of the most complex problems in science and engineering due to the strong nonlinear dynamics and multiscale properties of fluid flows (Hussain 1986)
Reduced representation of the flow field using approaches such as the proper orthogonal decomposition (POD; Lumley 1967) and dynamic mode decomposition (DMD; Schmid 2010) has shown these tools as having great ability to extract the dominant features of the flow (Taira et al 2017, 2020)
The network framework considered the vortical elements in a flow as nodes of a vortical interaction network
Summary
Analysis of turbulence remains as one of the most complex problems in science and engineering due to the strong nonlinear dynamics and multiscale properties of fluid flows (Hussain 1986). For modelling and controlling their dynamics, it is important to understand the interactions amongst the vortical structures Insights from such endeavors can support applications including flow separation control (Bhattacharjee, Scheelke & Troutt 1986) and mixing enhancement (Spencer & Wiley 1951). Community detection has been used to extract vortical structures from complex flows (Murayama et al 2018) and form reduced-order models for laminar wake flows (Gopalakrishnan Meena, Nair & Taira 2018; Gopalakrishnan Meena 2020). We use the intraand inter-cluster interactions extracted from a network-based framework for identifying important flow-modifying vortical structures. The interactions amongst the communities are used to identify key turbulent flow-modifying structures The goal of this network-based framework is not to alter the global turbulent flow, but to influence certain key vortical structures in the complex background of isotropic turbulence.
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