Similarity scaling of jet noise sources: towards a robust low-order jet noise scheme based on the Goldstein generalized acoustic analogy

2016 ◽  
Author(s):  
Vasily Semiletov ◽  
Sergey A. Karabasov
Keyword(s):  
Jet Noise ◽  
Acoustic Analogy ◽  
Noise Sources ◽  
Similarity Scaling ◽  
Low Order

scholarly journals Similarity scaling of jet noise sources for low-order jet noise modelling based on the Goldstein generalised acoustic analogy

2017 ◽  
Vol 16 (6) ◽  
pp. 476-490 ◽  
Author(s):  
Vasily A Semiletov ◽  
Sergey A Karabasov
Keyword(s):  
Jet Noise ◽  
Field Measurements ◽  
Noise Prediction ◽  
Far Field ◽  
Acoustic Analogy ◽  
Noise Sources ◽  
Modelling Framework ◽  
Noise Data ◽  
Field Noise ◽  
Low Order

As a first step towards a robust low-order modelling framework that is free from either calibration parameters based on the far-field noise data or any assumptions about the noise source structure, a new low-order noise prediction scheme is implemented. The scheme is based on the Goldstein generalised acoustic analogy and uses the Large Eddy Simulation database of fluctuating Reynolds stress fields from the CABARET MILES solution of Semiletov et al. corresponding to a static isothermal jet from the SILOET experiment for reconstruction of effective noise sources. The sources are scaled in accordance with the physics-based arguments and the corresponding sound meanflow propagation problem is solved using a frequency domain Green’s function method for each jet case. Results of the far-field noise predictions of the new method are validated for the two NASA SHJAR jet cases, sp07 and sp03 from and compared with the reference predictions, which are obtained by applying the Lighthill acoustic analogy scaling for the SILOET far-field measurements and using an empirical jet-noise prediction code, sJet.

Jet Noise Prediction via Low-order Machine Learning

2019 ◽  
Author(s):  
Christopher J. Ruscher ◽  
Sivaram Gogineni ◽  
Andrew S. Tenney ◽  
Mark N. Glauser
Keyword(s):  
Machine Learning ◽  
Jet Noise ◽  
Noise Prediction ◽  
Low Order

scholarly journals Jet-Surface Interaction Test: Phased Array Noise Source Localization Results

2012 ◽  
Author(s):  
Gary G. Podboy
Keyword(s):  
Source Localization ◽  
Phased Array ◽  
Noise Source ◽  
Jet Noise ◽  
Companion Paper ◽  
Far Field ◽  
Sound Waves ◽  
Axial Distribution ◽  
Noise Sources ◽  
Interaction Test

An experiment was conducted to investigate the effect that a planar surface located near a jet flow has on the noise radiated to the far-field. Two different configurations were tested: 1) a shielding configuration in which the surface was located between the jet and the far-field microphones, and 2) a reflecting configuration in which the surface was mounted on the opposite side of the jet, and thus the jet noise was free to reflect off the surface toward the microphones. Both conventional far-field microphone and phased array noise source localization measurements were obtained. This paper discusses phased array results, while a companion paper discusses far-field results. The phased array data show that the axial distribution of noise sources in a jet can vary greatly depending on the jet operating condition and suggests that it would first be necessary to know or be able to predict this distribution in order to be able to predict the amount of noise reduction to expect from a given shielding configuration. The data obtained on both subsonic and supersonic jets show that the noise sources associated with a given frequency of noise tend to move downstream, and therefore, would become more difficult to shield, as jet Mach number increases. The noise source localization data obtained on cold, shock-containing jets suggests that the constructive interference of sound waves that produces noise at a given frequency within a broadband shock noise hump comes primarily from a small number of shocks, rather than from all the shocks at the same time. The reflecting configuration data illustrates that the law of reflection must be satisfied in order for jet noise to reflect off of a surface to an observer, and depending on the relative locations of the jet, the surface, and the observer, only some of the jet noise sources may satisfy this requirement.

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

Acoustics ◽  
2020 ◽  
Vol 2 (3) ◽  
pp. 617-649
Author(s):  
Sébastien Guérin ◽  
Carolin Kissner ◽  
Pascal Seeler ◽  
Ricardo Blázquez ◽  
Pedro Carrasco Laraña ◽  
...  
Keyword(s):  
Analytical Methods ◽  
Acoustic Waves ◽  
Power Spectra ◽  
Low Frequency ◽  
Broadband Noise ◽  
Experimental Results ◽  
Acoustic Analogy ◽  
Noise Sources ◽  
Acoustic Models ◽  
The Impact

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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