Data fusion to improve supersonic jet data

Christopher J. Ruscher, Sivaram Gogineni, Andrew S. Magstadt, Matthew G. Berry, Mark N Glauser

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

2 Scopus citations

Abstract

Jet noise is a problem that affects department of defense and civilian flight operations. To design quieter aircraft, low-order models are necessary to predict the noise produced by an engine during the early design phase. Accurate development of these models requires high-quality, high-speed data. However, current simulation and measurement techniques may not always capture the necessary data. Through the fusion of computational and experimental data, one can improve the quality of data. Fusion was applied to estimate areas in the experimental data affected by occlusions, to account for the short time records of the computational data, and the lack of temporal resolution in the experiments.

Original languageEnglish (US)
Title of host publicationAIAA Aerospace Sciences Meeting
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
Edition210059
ISBN (Print)9781624105241
DOIs
StatePublished - Jan 1 2018
EventAIAA Aerospace Sciences Meeting, 2018 - Kissimmee, United States
Duration: Jan 8 2018Jan 12 2018

Other

OtherAIAA Aerospace Sciences Meeting, 2018
CountryUnited States
CityKissimmee
Period1/8/181/12/18

ASJC Scopus subject areas

  • Aerospace Engineering

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