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Article

ACAT1 Benchmark of RANS-Informed Analytical Methods for Fan Broadband Noise Prediction: Part II—Influence of the Acoustic Models

1
Department of Engine Acoustics, Institute of Propulsion Technology, German Aerospace Center (DLR), 10623 Berlin, Germany
2
Department of Engine Propulsion and Fluid Dynamics, Universidad Politécnica de Madrid (UPM), 28040 Madrid, Spain
3
Aerodynamic Technology Department, ITP Aero, 28108 Alcobendas, Spain
4
Aerodynamics and Acoustics Department, Safran Aircraft Engines, 77550 Moissy-Cramayel, France
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CNRS, Laboratoire de Mécanique des Fluides et d’Acoustique, INSA Lyon, Univ. Lyon, Université Claude Bernard Lyon I, École Centrale de Lyon, 69134 Écully, France
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Institute of Sound and Vibration Research, University of Southampton, Southampton SO17 1BJ, UK
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Department of Aerodynamics, Aeroelasticity, and Acoustics, ONERA—The French Aerospace Lab, 92322 Châtillon, France
8
Airbus Commercial Aircraft, Acoustics Methods, 31060 Toulouse, France
*
Author to whom correspondence should be addressed.
Acoustics 2020, 2(3), 617-649; https://doi.org/10.3390/acoustics2030033
Received: 19 June 2020 / Revised: 11 August 2020 / Accepted: 13 August 2020 / Published: 16 August 2020
(This article belongs to the Special Issue Aeroacoustics of Turbomachines)
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. View Full-Text
Keywords: RANS-informed noise prediction; fan broadband noise; ACAT1 fan benchmark RANS-informed noise prediction; fan broadband noise; ACAT1 fan benchmark
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MDPI and ACS Style

Guérin, S.; Kissner, C.; Seeler, P.; Blázquez, R.; Carrasco Laraña, P.; de Laborderie, H.; Lewis, D.; Chaitanya, P.; Polacsek, C.; Thisse, J. ACAT1 Benchmark of RANS-Informed Analytical Methods for Fan Broadband Noise Prediction: Part II—Influence of the Acoustic Models. Acoustics 2020, 2, 617-649. https://doi.org/10.3390/acoustics2030033

AMA Style

Guérin S, Kissner C, Seeler P, Blázquez R, Carrasco Laraña P, de Laborderie H, Lewis D, Chaitanya P, Polacsek C, Thisse J. ACAT1 Benchmark of RANS-Informed Analytical Methods for Fan Broadband Noise Prediction: Part II—Influence of the Acoustic Models. Acoustics. 2020; 2(3):617-649. https://doi.org/10.3390/acoustics2030033

Chicago/Turabian Style

Guérin, Sébastien, Carolin Kissner, Pascal Seeler, Ricardo Blázquez, Pedro Carrasco Laraña, Hélène de Laborderie, Danny Lewis, Paruchuri Chaitanya, Cyril Polacsek, and Johan Thisse. 2020. "ACAT1 Benchmark of RANS-Informed Analytical Methods for Fan Broadband Noise Prediction: Part II—Influence of the Acoustic Models" Acoustics 2, no. 3: 617-649. https://doi.org/10.3390/acoustics2030033

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