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ACAT1 benchmark of RANS-informed analytical methods for fan broadband noise prediction: Part II - Influence of the acoustic models

ACAT1 benchmark of RANS-informed analytical methods for fan broadband noise prediction: Part II - Influence of the acoustic models
ACAT1 benchmark of RANS-informed analytical methods for fan broadband noise prediction: Part II - Influence of the acoustic models
A benchmark dedicated to RANS-informed analytical methods for the prediction of turbofan rotor–stator interaction broadband noise was organised within the framework of the European project TurboNoiseBB. The second part of this benchmark focuses on the impact of the acoustic models. Twelve different approaches implemented in seven different acoustic solvers are compared. Some of the methods resort to the acoustic analogy, while some use a direct approach bypassing the calculation of a source term. Due to differing application objectives, the studied methods vary in terms of complexity to represent the turbulence, to calculate the acoustic response of the stator and to model the boundary and flow conditions for the generation and propagation of the acoustic waves. This diversity of approaches constitutes the unique quality of this work. The overall agreement of the predicted sound power spectra is satisfactory. While the comparison between the models show significant deviations at low frequency, the power levels vary within an interval of ±3 dB at mid and high frequencies. The trends predicted by increasing the rotor speed are similar for almost all models. However, most predicted levels are some decibels lower than the experimental results. This comparison is not completely fair—particularly at low frequency—because of the presence of noise sources in the experimental results, which were not considered in the simulations.
617-649
Guérin, Sébastien
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Kissner, Carolin
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Seeler, Pascal
82aa87ed-9c32-43a3-9e64-b5cc6fb77789
Blázquez, Ricardo
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Laraña, Pedro Carrasco
c1959cfe-6808-41f9-a03c-87cb0f793c4e
Laborderie, Hélène de
58cb71f7-0be0-4962-884c-2db7266d5a4f
Lewis, Danny
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Paruchuri, Chaitanya
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Polacsek, Cyril
ce796664-12b3-4def-b61d-300997b3026f
Thisse, Johan
5bd3159d-1f47-494f-a856-540337ebd43b
Guérin, Sébastien
b97f1039-1c2b-4e0b-9030-a348089d178c
Kissner, Carolin
4ae965b4-29b3-475a-86d0-27bf93f4e11c
Seeler, Pascal
82aa87ed-9c32-43a3-9e64-b5cc6fb77789
Blázquez, Ricardo
69b1fe42-1bbc-4e88-89e8-77bd40d5a1fa
Laraña, Pedro Carrasco
c1959cfe-6808-41f9-a03c-87cb0f793c4e
Laborderie, Hélène de
58cb71f7-0be0-4962-884c-2db7266d5a4f
Lewis, Danny
1626add9-560a-4a62-a7a0-b0f64f8ba9ae
Paruchuri, Chaitanya
5c1def64-6347-4be3-ac2d-b9f6a314b81d
Polacsek, Cyril
ce796664-12b3-4def-b61d-300997b3026f
Thisse, Johan
5bd3159d-1f47-494f-a856-540337ebd43b

Guérin, Sébastien, Kissner, Carolin, Seeler, Pascal, Blázquez, Ricardo, Laraña, Pedro Carrasco, Laborderie, Hélène de, Lewis, Danny, Paruchuri, Chaitanya, Polacsek, Cyril and Thisse, Johan (2020) ACAT1 benchmark of RANS-informed analytical methods for fan broadband noise prediction: Part II - Influence of the acoustic models. Acoustics, 2 (3), 617-649. (doi:10.3390/acoustics2030033).

Record type: Article

Abstract

A benchmark dedicated to RANS-informed analytical methods for the prediction of turbofan rotor–stator interaction broadband noise was organised within the framework of the European project TurboNoiseBB. The second part of this benchmark focuses on the impact of the acoustic models. Twelve different approaches implemented in seven different acoustic solvers are compared. Some of the methods resort to the acoustic analogy, while some use a direct approach bypassing the calculation of a source term. Due to differing application objectives, the studied methods vary in terms of complexity to represent the turbulence, to calculate the acoustic response of the stator and to model the boundary and flow conditions for the generation and propagation of the acoustic waves. This diversity of approaches constitutes the unique quality of this work. The overall agreement of the predicted sound power spectra is satisfactory. While the comparison between the models show significant deviations at low frequency, the power levels vary within an interval of ±3 dB at mid and high frequencies. The trends predicted by increasing the rotor speed are similar for almost all models. However, most predicted levels are some decibels lower than the experimental results. This comparison is not completely fair—particularly at low frequency—because of the presence of noise sources in the experimental results, which were not considered in the simulations.

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Accepted/In Press date: 13 August 2020
Published date: 16 August 2020

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Local EPrints ID: 443398
URI: http://eprints.soton.ac.uk/id/eprint/443398
PURE UUID: 2ee651da-5bc2-4ff7-9466-4a1d66b58747

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Date deposited: 24 Aug 2020 16:33
Last modified: 16 Mar 2024 08:41

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Contributors

Author: Sébastien Guérin
Author: Carolin Kissner
Author: Pascal Seeler
Author: Ricardo Blázquez
Author: Pedro Carrasco Laraña
Author: Hélène de Laborderie
Author: Danny Lewis
Author: Cyril Polacsek
Author: Johan Thisse

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