Integration of non-time-resolved PIV and time-resolved velocity point sensors for dynamic estimation of velocity fields

Jonathan H. Tu, John Griffin, Adam Hart, Clarence Worth Rowley, Louis N. Cattafesta, Lawrence S. Ukeiley

Research output: Contribution to journalArticlepeer-review

49 Scopus citations

Abstract

We demonstrate a three-step method for estimating time-resolved velocity fields using time-resolved point measurements and non-time-resolved particle image velocimetry data. A variant of linear stochastic estimation is used to obtain an initial set of time-resolved estimates of the flow field. These estimates are then used to identify a linear model of the flow dynamics. The model is incorporated into a Kalman smoother, which provides an improved set of estimates. We verify this method with an experimental study of the wake behind an elliptical-leading-edge flat plate at a thickness Reynolds number of 3,600. We find that, for this particular flow, the Kalman smoother estimates are more accurate and more robust to noise than the initial, stochastic estimates. Consequently, dynamic mode decomposition more accurately identifies coherent structures in the flow when applied to the Kalman smoother estimates. Causal implementations of the estimators, which are necessary for flow control, are also investigated. Similar outcomes are observed, with model-based estimation outperforming stochastic estimation, though the advantages are less pronounced.

Original languageEnglish (US)
JournalExperiments in Fluids
Volume54
Issue number2
DOIs
StatePublished - Feb 2013

All Science Journal Classification (ASJC) codes

  • Computational Mechanics
  • Mechanics of Materials
  • Physics and Astronomy(all)
  • Fluid Flow and Transfer Processes

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