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A fast prediction method for rotor buzz-saw noise based on an analytical approach

: Moreau, Antoine; Staggat, Martin; Gscheidle, Christian

Volltext urn:nbn:de:0011-n-5523631 (66 KByte PDF) - This publication has been withdrawn by the institute.
MD5 Fingerprint: 9ec21da0eef31463d9365aedccde858b
Erstellt am: 25.7.2019

Aerospace Research Central -ARC-; American Institute of Aeronautics and Astronautics -AIAA-, Washington/D.C.; Council of European Aerospace Societies -CEAS-:
25th AIAA/CEAS Aeroacoustics Conference 2019 : 20-23 May 2019, Delft, The Netherlands
Salzburg: ARC, 2019
ISBN: 978-1-62410-588-3
Paper AIAA 2019-2641, 24 S.
Aeroacoustics Conference <25, 2019, Delft>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer SCAI ()
prediction method; rotor buzz-saw noise; analytical approach

The present work proposes a time-domain two-dimensional radial-strip approach for the fast prediction of buzz-saw noise that is emitted by supersonic and transonic rotors. The first part of the paper gives an overview on existing prediction models related to this problem and motivates the need for an extended analytical prediction including the properties of the shocks at their generation. The fan application considered to validate the model is also presented in this part, as well as the numerical set-up of the quasi-3D RANS simulations to be compared with the model. In the second part, the analytical model is first established for the case where all blades are identical. In this part, two new relations for the initial shock strength and the initial shock positions are presented. Their domain of application extends from the subsonic to the supersonic regimes. Finally in the third part of the paper, the model is extended to the more general case with non-identical blades. A new model correlating analytically the blade angle deviations with the individual shock positions is derived and discussed. It is shown that despite strong assumptions, the analytical predictions are able to reproduce properly the main results obtained from the RANS simulations.