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ACAT1 Benchmark of RANS-Informed Analytical Methods for Fan Broadband Noise Prediction-Part I-Influence of the RANS Simulation

Kissner, Carolin (author)
Deutsches Zentrums für Luft- und Raumfahrt (DLR),German Aerospace Center (DLR)
Guerin, Sebastien (author)
Deutsches Zentrums für Luft- und Raumfahrt (DLR),German Aerospace Center (DLR)
Seeler, Pascal (author)
Deutsches Zentrums für Luft- und Raumfahrt (DLR),German Aerospace Center (DLR)
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Billson, Mattias (author)
GKN Aerospace Sweden AB
Chaitanya, Paruchuri (author)
University of Southampton
Larana, Pedro Carrasco (author)
de Laborderie, Helene (author)
SAFRAN Aircraft Engines
Francois, Benjamin (author)
ONERA - The French Aerospace Lab
Lefarth, Katharina (author)
MTU Aero Engines AG
Lewis, Danny (author)
Université de Lyon
Montero Villar, Gonzalo, 1992 (author)
Chalmers tekniska högskola,Chalmers University of Technology
Node-Langlois, Thomas (author)
Airbus Group,Airbus Group Ltd
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 (creator_code:org_t)
2020-07-22
2020
English.
In: Acoustics. - : MDPI AG. - 2624-599X. ; 2:3, s. 539-578
  • Journal article (peer-reviewed)
Abstract Subject headings
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  • A benchmark of Reynolds-Averaged Navier-Stokes (RANS)-informed analytical methods, which are attractive for predicting fan broadband noise, was conducted within the framework of the European project TurboNoiseBB. This paper discusses the first part of the benchmark, which investigates the influence of the RANS inputs. Its companion paper focuses on the influence of the applied acoustic models on predicted fan broadband noise levels. While similar benchmarking activities were conducted in the past, this benchmark is unique due to its large and diverse data set involving members from more than ten institutions. In this work, the authors analyze RANS solutions performed at approach conditions for the ACAT1 fan. The RANS solutions were obtained using different CFD codes, mesh resolutions, and computational settings. The flow, turbulence, and resulting fan broadband noise predictions are analyzed to pinpoint critical influencing parameters related to the RANS inputs. Experimental data are used for comparison. It is shown that when turbomachinery experts perform RANS simulations using the same geometry and the same operating conditions, the most crucial choices in terms of predicted fan broadband noise are the type of turbulence model and applied turbulence model extensions. Chosen mesh resolutions, CFD solvers, and other computational settings are less critical.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Maskinteknik -- Farkostteknik (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Mechanical Engineering -- Vehicle Engineering (hsv//eng)
TEKNIK OCH TEKNOLOGIER  -- Maskinteknik -- Strömningsmekanik och akustik (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Mechanical Engineering -- Fluid Mechanics and Acoustics (hsv//eng)
TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)

Keyword

fan broadband noise
turbulence models
ACAT1 fan
fan noise benchmark
RANS-informed noise prediction

Publication and Content Type

art (subject category)
ref (subject category)

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