2014
DOI: 10.5028/jatm.v6i1.258
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Noise Source Distribution of Coaxial Subsonic Jet-Short-Cowl Nozzle

Abstract: The noise source distribution of a short-cowl coaxial jet operating at different velocity ratios is described in this work. This was motivated by an ongoing research about noise prediction of coaxial jets through Acoustic Analogy with purposes of industrial engine application. This research has been carried out between Universidade Federal de Uberlândia (UFU), Brazil and the Institute of Sound and Vibration Research (ISVR) at Southampton University, UK. The numerical approach employed is originally based on Li… Show more

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Cited by 3 publications
(1 citation statement)
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“…Hence, from the practical viewpoint, when the simulation time is an important factor, there is a question what is the "value for money" of LES in comparison with the fast-turn-around-time RANS methods. For example, the low cost of RANS flow solutions methods explain the popularity of low-fidelity methods for coaxial jet flow and noise calculation such as a combination of the RANS solutions and the Lighthill acoustic analogy [15] as well as a semi-analytical investigation of the hydrodynamic stability and noise generation mechanisms in CoJeN jets [16]. Furthermore, RANS solutions of the CoJeN jets were used for noise predictions based on the Goldstein generalized acoustic analogy in [18].…”
Section: Introductionmentioning
confidence: 99%
“…Hence, from the practical viewpoint, when the simulation time is an important factor, there is a question what is the "value for money" of LES in comparison with the fast-turn-around-time RANS methods. For example, the low cost of RANS flow solutions methods explain the popularity of low-fidelity methods for coaxial jet flow and noise calculation such as a combination of the RANS solutions and the Lighthill acoustic analogy [15] as well as a semi-analytical investigation of the hydrodynamic stability and noise generation mechanisms in CoJeN jets [16]. Furthermore, RANS solutions of the CoJeN jets were used for noise predictions based on the Goldstein generalized acoustic analogy in [18].…”
Section: Introductionmentioning
confidence: 99%