A Survey of Machine Learning in Rocket Propulsion Applications

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

This paper examines the current state of machine learning and related techniques with regard to their application to propulsion applications with an emphasis on rockets. A survey of the current research literature will be provided, examining the different machine learning tools and how they are applied to propulsive applications. In addition to this review, challenges to adoption of machine learning tools will be examined both in general and in propulsion specific contexts. The availability of high-fidelity data is a challenge of critical importance, especially in the area of propulsionrelated research, as machine learning algorithms require extensive training data to be effective. Finally, a review of potential areas for expansion will also be evaluated for their suitability for machine learning and related tools. Of particular interest are the areas of generative design and physics-informed machine learning which may provide increased propulsion system performance due to alternate designs that may not have been considered and additional physical insight into the complex physics of propulsive flows.

Original languageEnglish (US)
Title of host publicationAIAA Aviation and Aeronautics Forum and Exposition, AIAA AVIATION Forum 2023
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)9781624107047
DOIs
StatePublished - 2023
EventAIAA Aviation and Aeronautics Forum and Exposition, AIAA AVIATION Forum 2023 - San Diego, United States
Duration: Jun 12 2023Jun 16 2023

Publication series

NameAIAA Aviation and Aeronautics Forum and Exposition, AIAA AVIATION Forum 2023

Conference

ConferenceAIAA Aviation and Aeronautics Forum and Exposition, AIAA AVIATION Forum 2023
Country/TerritoryUnited States
CitySan Diego
Period6/12/236/16/23

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Nuclear Energy and Engineering
  • Aerospace Engineering

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