A deep learning approach to jet noise prediction

Andrew S. Tenney, Mark N. Glauser, Jacques Lewalle, Christopher J. Ruscher

Research output: Chapter in Book/Entry/PoemConference contribution

14 Scopus citations


This study is an exploration of a deep learning technique as it applies to jet noise prediction and modeling. A database was created describing the near-field and far-field conditions of a complex non-axisymmetric jet flow, with Mach numbers ranging from 1.0 to 1.6. The problem was posed as a form of multivariate nonlinear regression, and a Deep Neural Network (DNN) was used to create a model. Google’s TensorFlow was utilized in network construction and training, and a feature space consisting of plausible predictors of far-field directional SPL was defined, based on previous fundamental studies and jet noise scaling laws. The effects of feature space, learning rate, and network architecture modification were also studied and quantified. On average, the DNN was able to predict directional far-field SPL within ±0.75dB, surpassing our original goals. In addition, we suggest ways to extend this technique to encompass a greater range of nozzle geometries and flow conditions.

Original languageEnglish (US)
Title of host publicationAIAA Aerospace Sciences Meeting
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)9781624105241
StatePublished - 2018
EventAIAA Aerospace Sciences Meeting, 2018 - Kissimmee, United States
Duration: Jan 8 2018Jan 12 2018

Publication series

NameAIAA Aerospace Sciences Meeting, 2018


OtherAIAA Aerospace Sciences Meeting, 2018
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Aerospace Engineering


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