Identifying vortical network connectors for turbulent flow modification

Meena, Muralikrishnan Gopalakrishnan; Taira, Kunihiko

doi:10.1017/jfm.2021.35

Cited by 22 publications

(11 citation statements)

References 80 publications

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“…For the construction of the vortical network, we collect vorticity snapshots at a constant time interval ∆t k = 10∆t over a downsampled mesh with the cell size ∆x n = 2∆x, for which we observed no significant difference in the broadcast modes for downsampling up to ∆x n = 4∆x. We treat the vortical element residing in each Cartesian cell as a node in the network and the interactions between the vortical elements as the edges, as shown in figure 1 (Taira et al 2016;Gopalakrishnan Meena & Taira 2020). The time-varying nature of the decaying turbulence is characterized by the varying interactions amongst the vortical elements, resulting in a time-evolving vortical network.…”

Section: Model Problem Setupmentioning

confidence: 99%

“…with A ii = 0 since there is no induced velocity by an vortical element on itself. This Biot-Savart edge weight has been used for network-theoretic models of vortical flows (Nair & Taira 2015;Taira et al 2016;Gopalakrishnan Meena & Taira 2020).…”

Section: Edge Weights: Biot-savart Network and Navier-stokes Networkmentioning

confidence: 99%

“…In a sea of vortices, a dense network of vortical interactions gives rise to their complex dynamics. For the characterization, modeling, and control of such vortical flow, the identification of flow structures influential to the overall governing dynamics is critical (Schlueter-Kuck & Dabiri 2017;Jiménez 2018Jiménez , 2020Taira et al 2016;Gopalakrishnan Meena & Taira 2020). Due to the large degrees of freedom in describing complex vortical flows, tremendous efforts have been placed on utilizing modal analysis techniques to extract the key flow structures in a low-order representation (Holmes et al 2012;Schmid 2010;Taira et al 2017Taira et al , 2020.…”

Section: Introductionmentioning

confidence: 99%

“…For the characterization, modeling, and control of such vortical flow, the identification of flow structures influential to the overall governing dynamics is critical (Schlueter-Kuck & Dabiri 2017;Jiménez 2018Jiménez , 2020Taira et al 2016;Gopalakrishnan Meena & Taira 2020). Due to the large degrees of freedom in describing complex vortical flows, tremendous efforts have been placed on utilizing modal analysis techniques to extract the key flow structures in a low-order representation (Holmes et al 2012;Schmid 2010;Taira et al 2017Taira et al , 2020. By revealing the influential structures, the actuation can be applied wisely to modify the overall vortical flow dynamics efficiently and effectively, providing pathways to improve operations of fluid based engineering systems.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Network broadcast analysis and control of turbulent flows

Yeh,

Meena,

Taira

2020

Preprint

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We present a network-based modal analysis that identifies the key dynamical paths along which perturbations amplify in an isotropic turbulent flow. This analysis is built upon the Katz centrality, which reveals the flow structures that can effectively spread perturbations over the time-evolving network of vortical elements that constitute the turbulent flow field. Motivated by the resolvent form of the Katz function, we take the singular value decomposition of the resulting communicability matrix, complementing resolvent analysis. The right-singular vector, referred to as the broadcast mode, gives insights into the sensitive regions where introduced perturbations can be effectively spread over the entire fluid-flow network as it evolves over time. The broadcast mode reveals that vortex dipoles are the important structures in spreading perturbations. By perturbing the flow with the broadcast mode, we demonstrate its capability to effectively modify the evolution of turbulent flows. The current networkinspired work presents a novel use of network analysis to guide flow control efforts, in particular for time-varying turbulent base flows.

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Section: Model Problem Setupmentioning

confidence: 99%

Section: Edge Weights: Biot-savart Network and Navier-stokes Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Network broadcast analysis and control of turbulent flows

Yeh,

Meena,

Taira

2020

Preprint

Self Cite

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“…In order to fully account for the effects deriving from such realistic conditions, and to exploit only a sparse set of sensors-hence, without the knowledge of additional parameters such as the instantaneous angle of attack-existing data-driven methods are continuously improved, as well as novel approaches are proposed. Among other techniques, complex networks represent a powerful and versatile tool for time-series analysis 14 that have been recently employed to study fluid flows, 15 including vortical flows, 16,17 turbulent-combustor dynamics, [18][19][20] as well as mixing in wall-bounded turbulence. 21,22 In this context, transition networks-thanks to their connection with Markov models 14 -have been successfully employed for time series reconstruction, [23][24][25][26] as well as for reduced-order modeling [27][28][29] and control.…”

Section: Introductionmentioning

confidence: 99%

Load estimation in unsteady flows from sparse pressure measurements: Application of transition networks to experimental data

2022

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Inspired by biological swimming and flying with distributed sensing, we propose a data-driven approach for load estimation that relies on complex networks. We exploit sparse, real-time pressure inputs, combined with pre-trained transition networks, to estimate aerodynamic loads in unsteady and highly separated flows. The transition networks contain the aerodynamic states of the system as nodes along with the underlying dynamics as links. A weighted average-based (WAB) strategy is proposed and tested on realistic experimental data on the flow around an accelerating elliptical plate at various angles of attack. Aerodynamic loads are then estimated for angles-of-attack cases not included in the training dataset so as to simulate the estimation process. An optimization process is also included to account for the system's temporal dynamics. Performance and limitations of the WAB approach are discussed, showing that transition networks can represent a versatile and effective data-driven tool for real-time signal estimation using sparse and noisy signals (such as surface pressure) in realistic flows.

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Network-theoretic modeling of fluid–structure interactions

Nair,

Douglass,

Arya

2023

Theor. Comput. Fluid Dyn.

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The coupling interactions between deformable structures and unsteady fluid flows occur across a wide range of spatial and temporal scales in many engineering applications. These fluid-structure interactions (FSI) make it challenging to predict flow physics accurately. In the present work, two multi-layer network approaches are proposed that characterize the interactions between the fluid and structural layers for an incompressible laminar flow over a two-dimensional compliant flat plate at a 35-degrees angle of attack. In one approach, the wake vortices and bound vortexlets form the nodes of the network with the edges defined by induced velocity. In the other approach, coherent structures (fluid modes) contributing to the kinetic energy of the flow and structural modes contributing to the kinetic energy of the compliant structure constitute the network nodes. The energy transfers between the modes are extracted using a perturbation approach. The network structure of the FSI system is further simplified using the community detection algorithm in the vortical approach and by selecting dominant modes in the modal approach. Network measures are used to reveal the temporal behavior of the individual nodes within the simplified FSI system. Predictive models are then built using both data-driven and physics-based methods. We conclude by investigating the controllability of the modal interaction network. This work sets the foundation for network-theoretic reduced-order modeling of fluid-structure interactions, generalizable to other multi-physics systems.

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Identifying vortical network connectors for turbulent flow modification

Abstract: Abstract

Cited by 22 publications

References 80 publications

Network broadcast analysis and control of turbulent flows

Network broadcast analysis and control of turbulent flows

Load estimation in unsteady flows from sparse pressure measurements: Application of transition networks to experimental data

Network-theoretic modeling of fluid–structure interactions

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