Data-driven Sensor Placement for Fluid Flows

Palash Sashittal, Daniel J. Bodony

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Optimal sensor placement for fluid flows is an important and challenging problem. We propose a novel method for sensor placement that leverages recent advancements in data-driven reduced-order modeling techniques. The proposed methodology can be used in conjunction with any data-driven modeling technique that provides a linear approximation of the fluid dynamics. We use adjoint-based gradient descent to find sensor locations that minimize the trace of an approximation of the estimation error covariance matrix. We also propose an augmented objective function can is more suited for control-oriented applications. We demonstrate the performance of our method for reconstruction and prediction of the complex linearized Ginzburg-Landau equation in the globally unstable regime.

Original languageEnglish (US)
Title of host publicationAIAA Aviation and Aeronautics Forum and Exposition, AIAA AVIATION Forum 2021
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)9781624106101
DOIs
StatePublished - 2021
Externally publishedYes
EventAIAA Aviation and Aeronautics Forum and Exposition, AIAA AVIATION Forum 2021 - Virtual, Online
Duration: Aug 2 2021Aug 6 2021

Publication series

NameAIAA Aviation and Aeronautics Forum and Exposition, AIAA AVIATION Forum 2021

Conference

ConferenceAIAA Aviation and Aeronautics Forum and Exposition, AIAA AVIATION Forum 2021
CityVirtual, Online
Period8/2/218/6/21

ASJC Scopus subject areas

  • Aerospace Engineering
  • Energy Engineering and Power Technology
  • Nuclear Energy and Engineering

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