4.7 Article

A review of bias flow liners for acoustic damping in gas. turbine combustors

Journal

JOURNAL OF SOUND AND VIBRATION
Volume 400, Issue -, Pages 564-605

Publisher

ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jsv.2017.04.005

Keywords

Sound absorption; Liner; Bias flow; Combustion instability

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The optimized design of bias flow liner is a key element for the development of low emission combustion systems in modern gas turbines and aero-engines. The research of bias flow liners has a fairly long history concerning both the parameter dependencies as well as the methods to model the acoustic behaviour of bias flow liners under the variety of different bias and grazing flow conditions. In order to establish an overview over the state of the art, this paper provides a comprehensive review about the published research on bias flow liners and modelling approaches with an extensive study of the most relevant parameters determining the acoustic behaviour of these liners. The paper starts with a historical description of available investigations aiming on the characterization of the bias flow absorption principle. This chronological compendium is extended by the recent and ongoing developments in this field. In a next step the fundamental acoustic property of bias flow liner in terms of the wall impedance is introduced and the different derivations and formulations of this impedance yielding the different published model descriptions are explained and compared. Finally, a parametric study reveals the most relevant parameters for the acoustic damping behaviour of bias flow liners and how this is reflected by the various model representations. Although the general trend of the investigated acoustic behaviour is captured by the different models fairly well for a certain range of parameters, in the transition region between the resonance dominated and the purely bias flow related regime all models lack the correct damping prediction. This seems to be connected to the proper implementation of the reactance as a function of bias flow Mach number. (C) 2017 Elsevier Ltd. All rights reserved.

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