article

Reduced-Order Adaptive Controllers for Fluid Flows Using POD

Author:

S. S. RavindranAuthors Info & Claims

Journal of Scientific Computing, Volume 15, Issue 4

Pages 457 - 478

https://doi.org/10.1023/A:1011184714898

Published: 19 December 2000 Publication History

Abstract

This article presents a reduced-order adaptive controller design for fluid flows. Frequently, reduced-order models are derived from low-order bases computed by applying proper orthogonal decomposition (POD) on an a priori ensemble of data of the Navier–Stokes model. This reduced-order model is then used to derive a reduced-order controller. The approach discussed here differ from these approaches. It uses an adaptive procedure that improves the reduced-order model by successively updating the ensemble of data. The idea is to begin with an ensemble to form a reduced-order control problem. The resulting control is then applied back to the Navier–Stokes model to generate a new ensemble. This new ensemble then replaces the previous ensemble to derive a new reduced-order model. This iteration is repeated until convergence is achieved. The adaptive reduced-order controllers effectiveness in flow control applications is shown on a recirculation control problem in channel flow using blowing (actuation) on the boundary. Optimal placement for actuators is explored. Numerical implementations and results are provided illustrating the various issues discussed.

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Reduced-Order Adaptive Controllers for Fluid Flows Using POD

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Published In

cover image Journal of Scientific Computing

Journal of Scientific Computing Volume 15, Issue 4

December 2000

122 pages

ISSN:0885-7474

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Copyright © Copyright © 2000 Plenum Publishing Corporation.

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Plenum Press

United States

Publication History

Published: 19 December 2000

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Cited By

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https://dl.acm.org/doi/10.1016/j.camwa.2023.03.009
Abbaszadeh MDehghan MNavon I(2022)A POD reduced-order model based on spectral Galerkin method for solving the space-fractional Gray–Scott model with error estimateEngineering with Computers10.1007/s00366-020-01195-538:3(2245-2268)Online publication date: 1-Jun-2022
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Ştefănescu RSandu ANavon I(2015)POD/DEIM reduced-order strategies for efficient four dimensional variational data assimilationJournal of Computational Physics10.1016/j.jcp.2015.04.030295:C(569-595)Online publication date: 15-Aug-2015
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Burkardt JGunzburger MLee H(2006)Centroidal Voronoi Tessellation-Based Reduced-Order Modeling of Complex SystemsSIAM Journal on Scientific Computing10.1137/5106482750342221x28:2(459-484)Online publication date: 1-Jan-2006
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Couplet MBasdevant CSagaut P(2005)Calibrated reduced-order POD-Galerkin system for fluid flow modellingJournal of Computational Physics10.1016/j.jcp.2005.01.008207:1(192-220)Online publication date: 20-Jul-2005
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