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A dynamic observer to capture and control perturbation energy in noise amplifiers

Guzmán-Iñigo, J. ORCID: 0000-0002-1833-6034, Sipp, D. & Schmid, P. J. (2014). A dynamic observer to capture and control perturbation energy in noise amplifiers. Journal of Fluid Mechanics, 758, pp. 728-753. doi: 10.1017/jfm.2014.553


In this article, we introduce techniques to build a reduced-order model of a fluid system that accurately predicts the dynamics of a flow from local wall measurements. This is particularly difficult in the case of noise amplifiers where the upstream noise environment, triggering the flow via a receptivity process, is not known. A system identification approach, rather than a classical Galerkin technique, is used to extract the model from time-synchronous velocity snapshots and wall shear-stress measurements. The technique will be illustrated for the case of a transitional flat-plate boundary layer, where the snapshots of the flow are obtained from direct numerical simulations. Particular attention is directed to limiting the processed data to data that would be readily available in experiments, thus making the technique applicable to an experimental set-up. The proposed approach combines a reduction of the degrees of freedom of the system by a projection of the velocity snapshots onto a proper orthogonal decomposition basis combined with a system identification technique to obtain a state-space model. This model is then used in a feedforward control set-up to significantly reduce the kinetic energy of the perturbation field and thus successfully delay transition.

Publication Type: Article
Additional Information: This article has been published in a revised form in Journal of Fluid Mechanics This version is free to view and download for private research and study only. Not for re-distribution or re-use. © 2014 Cambridge University Press.
Subjects: Q Science > QC Physics
T Technology > TL Motor vehicles. Aeronautics. Astronautics
Departments: School of Science & Technology > Engineering
SWORD Depositor:
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