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

• Published in: Experiments in fluids 2013-02, Vol.54 (2), p.1-20, Article 1429
• Main Authors: Tu, Jonathan H., Griffin, John, Hart, Adam, Rowley, Clarence W., Cattafesta, Louis N., Ukeiley, Lawrence S.
• Format: Article
• Language: English
• Subjects: Computational fluid dynamics; Dynamics; Engineering; Engineering Fluid Dynamics; Engineering Thermodynamics; Estimates; Exact sciences and technology; Flat plates; Flow control; Fluid dynamics; Fluid flow; Fluid- and Aerodynamics; Fundamental areas of phenomenology (including applications); Heat and Mass Transfer; Instrumentation for fluid dynamics; Physics; Research Article; Stochasticity; Topics in Flow Control; Wakes
• ISSN: 0723-4864; 1432-1114
• DOI: 10.1007/s00348-012-1429-7

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.