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 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.

scholarly journals Application of Machine Learning to Include Honking Effect in Vehicular Traffic Noise Prediction

2021 ◽  
Vol 11 (13) ◽  
pp. 6030
Author(s):  
Daljeet Singh ◽  
Antonella B. Francavilla ◽  
Simona Mancini ◽  
Claudio Guarnaccia
Keyword(s):  
Machine Learning ◽  
Linear Models ◽  
Prediction Models ◽  
Road Traffic ◽  
Traffic Noise ◽  
Machine Learning Techniques ◽  
Coefficient Of Determination ◽  
Road Traffic Noise ◽  
Noise Prediction ◽  
Volume Percentage

A vehicular road traffic noise prediction methodology based on machine learning techniques has been presented. The road traffic parameters that have been considered are traffic volume, percentage of heavy vehicles, honking occurrences and the equivalent continuous sound pressure level. Leq A method to include the honking effect in the traffic noise prediction has been illustrated. The techniques that have been used for the prediction of traffic noise are decision trees, random forests, generalized linear models and artificial neural networks. The results obtained by using these methods have been compared on the basis of mean square error, correlation coefficient, coefficient of determination and accuracy. It has been observed that honking is an important parameter and contributes to the overall traffic noise, especially in congested Indian road traffic conditions. The effects of honking noise on the human health cannot be ignored and it should be included as a parameter in the future traffic noise prediction models.

scholarly journals Jet-Surface Interaction Test: Far-Field Noise Results

2013 ◽  
Vol 135 (7) ◽  
Author(s):  
Clifford A. Brown
Keyword(s):  
High Speed ◽  
Radial Distance ◽  
Jet Noise ◽  
Surface Interaction ◽  
Shielding Effect ◽  
Noise Prediction ◽  
Far Field ◽  
Wide Range ◽  
Field Noise ◽  
Surface Length

Many configurations proposed for the next generation of aircraft rely on the wing or other aircraft surfaces to shield the engine noise from the observers on the ground. However, the ability to predict the shielding effect and any new noise sources that arise from the high-speed jet flow interacting with a hard surface is currently limited. Furthermore, quality experimental data from jets with surfaces nearby suitable for developing and validating noise prediction methods are usually tied to a particular vehicle concept and, therefore, very complicated. The Jet-Surface Interaction Tests are intended to supply a high quality set of data covering a wide range of surface geometries and positions and jet flows to researchers developing aircraft noise prediction tools. The initial goal is to measure the noise of a jet near a simple planar surface while varying the surface length and location in order to: (1) validate noise prediction schemes when the surface is acting only as a jet noise shield and when the jet-surface interaction is creating additional noise, and (2) determine regions of interest for future, more detailed, tests. To meet these objectives, a flat plate was mounted on a two-axis traverse in two distinct configurations: (1) as a shield between the jet and the observer and (2) as a reflecting surface on the opposite side of the jet from the observer. The surface length was varied between 2 and 20 jet diameters downstream of the nozzle exit. Similarly, the radial distance from the jet centerline to the surface face was varied between 1 and 16 jet diameters. Far-field and phased array noise data were acquired at each combination of surface length and radial location using two nozzles operating at jet exit conditions across several flow regimes: subsonic cold, subsonic hot, underexpanded, ideally expanded, and overexpanded supersonic. The far-field noise results, discussed here, show where the jet noise is partially shielded by the surface and where jet-surface interaction noise dominates the low frequency spectrum as a surface extends downstream and approaches the jet plume.

Jet Noise Prediction: Validation and Physical Insight

2018 ◽  
Author(s):  
Wouter C. van der Velden ◽  
Damiano Casalino ◽  
Pradeep Gopalakrishnan ◽  
Avinash Jammalamadaka ◽  
Yanbing Li ◽  
...  
Keyword(s):  
Jet Noise ◽  
Noise Prediction ◽  
Physical Insight

scholarly journals Nozzles, turbulence, and jet noise prediction

2018 ◽  
Vol 860 ◽  
pp. 1-4 ◽  
Author(s):  
Jonathan B. Freund
Keyword(s):  
Prediction Accuracy ◽  
Jet Noise ◽  
Sound Field ◽  
Field Measurements ◽  
Detailed Comparison ◽  
Large Eddy Simulations ◽  
Noise Prediction ◽  
Large Eddy ◽  
Modelling Procedure ◽  
Flow Turbulence

Jet noise prediction is notoriously challenging because only subtle features of the flow turbulence radiate sound. The article by Brès et al. (J. Fluid Mech., vol. 851, 2018, pp. 83–124) shows that a well-constructed modelling procedure for the nozzle turbulence can provide unprecedented sub-dB prediction accuracy with modest-scale large-eddy simulations, as confirmed by detailed comparison with turbulence and sound-field measurements. This both illuminates the essential mechanisms of the flow and facilitates prediction for engineering design.

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