Time-dependent low-rank input–output operator for forced linearized dynamics with unsteady base flows

Understanding the linear growth of disturbances due to external forcing is crucial for flow stability analysis, flow control, and uncertainty quantification. These applications typically require a large number of forward simulations of the forced linearized dynamics, often in a brute-force fashion. When dealing with simple steady-state or periodic base flows, there exist powerful and cost-effective solution operator techniques. Once these solution operators are constructed, they can be used to determine the response to various forcings with negligible computational cost. However, these methods do not apply to problems with arbitrarily time-dependent base flows. This paper develops and investigates reduced-order modelling with time-dependent bases to build low-rank solution operators for forced linearized dynamics with arbitrarily time-dependent base flows. In particular, we use forced optimally time-dependent decomposition (f-OTD), which extracts the time-dependent correlated structures of the flow response to various excitations. Several demonstrations are included to illustrate the utility of the f-OTD low-rank approximation for performing global transient stability analysis. Additionally, we demonstrate the application of f-OTD in computing the post-transient response of linearized Navier–Stokes equations to a large number of impulses, which has applications in flow control.

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Publication Details

Journal
Journal of Fluid Mechanics
Published
2024-12-11
DOI
https://doi.org/10.1017/jfm.2024.1019
Citations
5
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
1.41

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article

Time-dependent low-rank input–output operator for forced linearized dynamics with unsteady base flows

Hessam Babaee, Alireza Amiri-Margavi
5 citations
Journal of Fluid Mechanics
Model Reduction and Neural Networks
1.41
article

Time-dependent low-rank input–output operator for forced linearized dynamics with unsteady base flows

Hessam Babaee, Alireza Amiri-Margavi
article en
5 citations

Abstract

Understanding the linear growth of disturbances due to external forcing is crucial for flow stability analysis, flow control, and uncertainty quantification. These applications typically require a large number of forward simulations of the forced linearized dynamics, often in a brute-force fashion. When dealing with simple steady-state or periodic base flows, there exist powerful and cost-effective solution operator techniques. Once these solution operators are constructed, they can be used to determine the response to various forcings with negligible computational cost. However, these methods do not apply to problems with arbitrarily time-dependent base flows. This paper develops and investigates reduced-order modelling with time-dependent bases to build low-rank solution operators for forced linearized dynamics with arbitrarily time-dependent base flows. In particular, we use forced optimally time-dependent decomposition (f-OTD), which extracts the time-dependent correlated structures of the flow response to various excitations. Several demonstrations are included to illustrate the utility of the f-OTD low-rank approximation for performing global transient stability analysis. Additionally, we demonstrate the application of f-OTD in computing the post-transient response of linearized Navier–Stokes equations to a large number of impulses, which has applications in flow control.

Journal of Fluid MechanicsVol. 1001
University of Pittsburgh (US)
National Science Foundation, National Aeronautics and Space Administration, University of Pittsburgh, Nuclear Safety and Security Commission, Langley Research Center, Center for Research Computing, University of Pittsburgh, Division of Chemical, Bioengineering, Environmental, and Transport Systems
Openalex Percentile: Top 18%
Model Reduction and Neural Networks
1.41
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Time-dependent low-rank input–output operator for forced linearized dynamics with unsteady base flows — Hessam Babaee, Alireza Amiri-Margavi · Journal of Fluid Mechanics (2024) | TGRS Research Map | TGRS