Determination of an Effective Sensor Configuration for Suppression of the von Karman Vortex Street

Abstract

A research program is aimed at suppressing the von Karman vortex street in the wake of a circular cylinder. This work investigated the issues of number and placement of sensors for real-time estimation of the reduced order states required for closing the loop. Sensor configuration design was based on data from a laminar Direct Navier Stokes, computational fluid dynamic (CFD) model. A low-dimensional Proper Orthogonal Decomposition (POD) was applied to the velocity component in the direction of the flow and sensor placement was based on the intensity of the resulting spatial eigenfunctions. A Linear Stochastic Estimator (LSE) was employed to map the velocity data to the temporal coefficients of the reduced order model. The effectiveness of the sensor configurations was validated experimentally at the water tunnel facility at USAFA at Reynolds numbers ranging from 82-99. For the unforced wake, a sample of 200 Particle Image Velocimetry (PIV) measurements was used. Results show that a 5 sensor configuration can keep the root mean square estimation error, for the first two mode amplitudes, to within 4% for the simulation data and within 10% in the case of noisy experimental measurements. This level of error is acceptable for a moderately robust controller.