Jet noise prediction via low-order machine learning

Christopher J. Ruscher, Sivaram Gogineni, Andrew Tenney, Mark Glauser

Research output: Chapter in Book/Entry/PoemConference contribution

1 Scopus citations


The intense acoustic emissions from supersonic jets can limit aircraft operation and cause serious hearing damage. It would therefore be beneficial to design low-noise engines. However, most of the factors that control noise production are decided early in the design process when it impractical to perform the simulations or experiments necessary to characterize acoustic emissions. As such, a practical design tool must be developed so engineers have the option to consider noise in future designs. In this work, a deep neural network (DNN) approach coupled with proper orthogonal decomposition (POD) has been explored as a method of predicting jet noise. It was found that coupling POD and DNN could produce a model capable of estimating noise production to within a few dB over a broad range of operating conditions while using a minimal amount of training data.

Original languageEnglish (US)
Title of host publicationAIAA Scitech 2019 Forum
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)9781624105784
StatePublished - Jan 1 2019
EventAIAA Scitech Forum, 2019 - San Diego, United States
Duration: Jan 7 2019Jan 11 2019

Publication series

NameAIAA Scitech 2019 Forum


ConferenceAIAA Scitech Forum, 2019
Country/TerritoryUnited States
CitySan Diego

ASJC Scopus subject areas

  • Aerospace Engineering


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