Linear predictive filtering in a numerically simulated turbulent flow

Abstract

A numerical investigation is made into the use of linear predictive (Wiener) filters to predict flow quantities in the near-wall region of a turbulent boundary layer for use in active control algorithms. Optimal filters for the prediction of Reynolds stress and fluctuating streamwise velocity components using wall-shear stress information are developed and their dependence on the number and location of shear sensors is explored. It is found that a densely populated, wall-based, sensor system can predict the near-wall Reynolds stress with good accuracy, and that 76% of the optimal performance can be achieved with as few as four sensors whose locations coincide with the strongest weights of the Wiener filter derived using a very dense network of input sensors.