Literature DB >> 30011576

Networked-oscillator-based modeling and control of unsteady wake flows.

Aditya G Nair1, Steven L Brunton2, Kunihiko Taira1.   

Abstract

A networked-oscillator-based analysis is performed to examine and control the transfer of kinetic energy for periodic bluff body flows. The dynamics of energy fluctuations in the flow field are described by a set of oscillators defined by conjugate pairs of spatial proper orthogonal decomposition (POD) modes. To extract the network of interactions among oscillators, impulse responses of the oscillators to amplitude and phase perturbations are tracked. Tracking small energy inputs and using linear regression, a networked-oscillator model is constructed that reveals energy exchange among the modes. The model captures the nonlinear interactions among the modal oscillators through a linear approximation. A large collection of system responses is aggregated to capture the general network structure of oscillator interactions. The present networked-oscillator model describes the modal perturbation dynamics more accurately than the empirical Galerkin reduced-order model. The linear network model for nonlinear dynamics is subsequently utilized to design a model-based feedback controller. The controller suppresses the modal amplitudes that result in wake unsteadiness leading to drag reduction. The strength of the proposed approach is demonstrated for a canonical example of two-dimensional unsteady flow over a circular cylinder. The present formulation enables the characterization of modal interactions to control fundamental energy transfers in unsteady bluff body flows.

Entities:  

Year:  2018        PMID: 30011576     DOI: 10.1103/PhysRevE.97.063107

Source DB:  PubMed          Journal:  Phys Rev E        ISSN: 2470-0045            Impact factor:   2.529


  3 in total

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Authors:  Megan Morrison; J Nathan Kutz
Journal:  IEEE Trans Netw Sci Eng       Date:  2020-10-19

2.  Sparsification of long range force networks for molecular dynamics simulations.

Authors:  Peter Woerner; Aditya G Nair; Kunihiko Taira; William S Oates
Journal:  PLoS One       Date:  2019-04-12       Impact factor: 3.240

3.  Randomized methods to characterize large-scale vortical flow networks.

Authors:  Zhe Bai; N Benjamin Erichson; Muralikrishnan Gopalakrishnan Meena; Kunihiko Taira; Steven L Brunton
Journal:  PLoS One       Date:  2019-11-18       Impact factor: 3.240

  3 in total

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