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A Machine Learning Approach to Jet-Surface Interaction Noise ModelingThis paper investigates using machine learning to rapidly develop empirical models suitable for system-level aircraft noise studies. In particular, machine learning is used to train a neural network to predict the noise spectra produced by a round jet near a surface over a range of surface lengths, surface standoff distances, jet Mach numbers, and observer angles. These spectra include two sources, jet-mixing noise and jet-surface interaction (JSI) noise, with different scale factors as well as surface shielding and reflection effects to create a multi- dimensional problem. A second model is then trained using data from three rectangular nozzles to include nozzle aspect ratio in the spectral prediction. The training and validation data are from an extensive jet-surface interaction noise database acquired at the NASA Glenn Research Center's Aero-Acoustic Propulsion Laboratory. Although the number of training and validation points is small compared a typical machine learning application, the results of this investigation show that this approach is viable if the underlying data are well behaved.
Document ID
20200000254
Acquisition Source
Glenn Research Center
Document Type
Conference Paper
Authors
Brown, Cliff
(NASA Glenn Research Center Cleveland, OH, United States)
Dowdall, Johnny
(NASA Intern)
Whiteaker, Brian
(NASA Intern)
Mcintyre, Lauren
(NASA Glenn Research Center Cleveland, OH, United States)
Miller, Chris
(NASA Glenn Research Center Cleveland, OH, United States)
Date Acquired
January 13, 2020
Publication Date
January 6, 2020
Subject Category
Aircraft Propulsion And Power
Computer Programming And Software
Report/Patent Number
GRC-E-DAA-TN75937
Meeting Information
Meeting: AIAA SciTech 2020
Location: Orlando, FL
Country: United States
Start Date: January 6, 2020
End Date: January 10, 2020
Sponsors: American Institute of Aeronautics and Astronautics (AIAA)
Funding Number(s)
WBS: 110076.02.03.04.40.01
Distribution Limits
Public
Copyright
Public Use Permitted.
Technical Review
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