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Passive Control for Turbofan Tonal Noise

Authors: B. Elhadidi; H. M. Atassi;

Passive Control for Turbofan Tonal Noise

Abstract

A three-dimensional linearized Euler model is developed for the interaction and scattering of unsteady incident disturbances by an annular cascade of unloaded blades. The model is first used to examine the validity of the popular two-dimensional strip theory for three-dimensional annular cascades at frequencies relevant to fan engine noise. It is then applied to investigate three methods of passive control for turbofan tonal noise, namely, blade lean, blade sweep, and mean flow acceleration. Because blade lean and sweep generally enhance the blade upwash radial phase variation, an analysis is carried out to examine the effects of the radial modulation of the incident disturbances on the annular cascade sectional lift and the modal acoustic response. Noise control by blade lean and sweep is then investigated for typical fans. The results show that blade lean or sweep can be designed to be effective noise reduction mechanisms; however, their effectiveness depends on the characteristics of the incident disturbances and the number of propagating duct modes. Converging ducts are also shown to be effective sound reduction mechanisms.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
32
Top 10%
Top 10%
Average
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