A Higher Order Artificial Compression Reduced Order Model for Control of Thermally Convective Flows

A second-order in time discretization of the artificial compression proper-orthogonal decomposition reduced-order model (POD-ROM) for control of thermally convective flows is presented. It is a velocity-pressure reduced-order model that does not require satisfaction of div-stability (Ladyzhenskaya-Babuska-Brezzi) condition for mixed reduced-order subspaces. Second-Order in time artificial compression POD-ROM approximations of solutions of the optimality system are defined and error estimates are derived. Numerical experiments are performed to validate the accuracy and efficiency of the scheme in solving a boundary control problem involving mixed convection flow.

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Publication Details

Published
2025-06-24
DOI
https://doi.org/10.23919/ecc65951.2025.11187117
Primary Topic
Model Reduction and Neural Networks
Type
article
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article

A Higher Order Artificial Compression Reduced Order Model for Control of Thermally Convective Flows

S. S. Ravindran
Model Reduction and Neural Networks
article

A Higher Order Artificial Compression Reduced Order Model for Control of Thermally Convective Flows

S. S. Ravindran
article en

Abstract

A second-order in time discretization of the artificial compression proper-orthogonal decomposition reduced-order model (POD-ROM) for control of thermally convective flows is presented. It is a velocity-pressure reduced-order model that does not require satisfaction of div-stability (Ladyzhenskaya-Babuska-Brezzi) condition for mixed reduced-order subspaces. Second-Order in time artificial compression POD-ROM approximations of solutions of the optimality system are defined and error estimates are derived. Numerical experiments are performed to validate the accuracy and efficiency of the scheme in solving a boundary control problem involving mixed convection flow.

University of Alabama in Huntsville (US)
Openalex Percentile: Top 77%
Model Reduction and Neural Networks
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