Data-driven sensor placement for fluid flows View Full Text


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Article Info

DATE

2021-08-30

AUTHORS

Palash Sashittal, Daniel J. Bodony

ABSTRACT

Optimal sensor placement for fluid flows is an important and challenging problem. In this study, we propose a completely data-driven and computationally efficient method for sensor placement. We use adjoint-based gradient descent to find the sensor location that minimizes the trace of an approximation of the estimation error covariance matrix. The proposed methodology can be used in conjunction with any reduced-order modeling technique that provides a linear approximation of the fluid dynamics. Moreover, the objective function can be augmented for different applications, which we illustrate by proposing a control-oriented objective function. We demonstrate the performance of our method for reconstruction and prediction of the complex linearized Ginzburg–Landau equation in the globally unstable regime. We also construct a low-dimensional observer-based feedback controller for the flow over an inclined flat plate that is able to suppress the wake vortex shedding in the presence of system and measurement noise. More... »

PAGES

709-729

References to SciGraph publications

  • 2017-04-17. De-biasing the dynamic mode decomposition for applied Koopman spectral analysis of noisy datasets in THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS
  • 2012-04-27. Variants of Dynamic Mode Decomposition: Boundary Condition, Koopman, and Fourier Analyses in JOURNAL OF NONLINEAR SCIENCE
  • 2019-10-29. Reduced-order control using low-rank dynamic mode decomposition in THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS
  • 1997-03. Nonlinear Programming in JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
  • 2020-02-23. Experimental Applications of the Koopman Operator in Active Learning for Control in THE KOOPMAN OPERATOR IN SYSTEMS AND CONTROL
  • 2020-02-23. Data-Driven Nonlinear Stabilization Using Koopman Operator in THE KOOPMAN OPERATOR IN SYSTEMS AND CONTROL
  • 2020-04-27. Data-driven selection of actuators for optimal control of airfoil separation in THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS
  • 2020-02-23. Feedback Control of Nonlinear PDEs Using Data-Efficient Reduced Order Models Based on the Koopman Operator in THE KOOPMAN OPERATOR IN SYSTEMS AND CONTROL
  • 2020-02-23. Koopman Framework for Nonlinear Estimation in THE KOOPMAN OPERATOR IN SYSTEMS AND CONTROL
  • 2016-02-22. Characterizing and correcting for the effect of sensor noise in the dynamic mode decomposition in EXPERIMENTS IN FLUIDS
  • 2020-02-23. Koopman Model Predictive Control of Nonlinear Dynamical Systems in THE KOOPMAN OPERATOR IN SYSTEMS AND CONTROL
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00162-021-00584-w

    DOI

    http://dx.doi.org/10.1007/s00162-021-00584-w

    DIMENSIONS

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