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 Citations (Scopus)

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

Fingerprint

Data fusion
Aircraft
Engines
Experiments

ASJC Scopus subject areas

  • Aerospace Engineering

Cite this

Ruscher, C. J., Gogineni, S., Magstadt, A. S., Berry, M. G., & Glauser, M. N. (2018). Data fusion to improve supersonic jet data. In AIAA Aerospace Sciences Meeting (210059 ed.). American Institute of Aeronautics and Astronautics Inc, AIAA. https://doi.org/10.2514/6.2018-1735

Data fusion to improve supersonic jet data. / Ruscher, Christopher J.; Gogineni, Sivaram; Magstadt, Andrew S.; Berry, Matthew G.; Glauser, Mark N.

AIAA Aerospace Sciences Meeting. 210059. ed. American Institute of Aeronautics and Astronautics Inc, AIAA, 2018.

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

Ruscher, CJ, Gogineni, S, Magstadt, AS, Berry, MG & Glauser, MN 2018, Data fusion to improve supersonic jet data. in AIAA Aerospace Sciences Meeting. 210059 edn, American Institute of Aeronautics and Astronautics Inc, AIAA, AIAA Aerospace Sciences Meeting, 2018, Kissimmee, United States, 1/8/18. https://doi.org/10.2514/6.2018-1735
Ruscher CJ, Gogineni S, Magstadt AS, Berry MG, Glauser MN. Data fusion to improve supersonic jet data. In AIAA Aerospace Sciences Meeting. 210059 ed. American Institute of Aeronautics and Astronautics Inc, AIAA. 2018 https://doi.org/10.2514/6.2018-1735
Ruscher, Christopher J. ; Gogineni, Sivaram ; Magstadt, Andrew S. ; Berry, Matthew G. ; Glauser, Mark N. / Data fusion to improve supersonic jet data. AIAA Aerospace Sciences Meeting. 210059. ed. American Institute of Aeronautics and Astronautics Inc, AIAA, 2018.
@inproceedings{9f941b3a6f89441c905bfc3c20311c2b,
title = "Data fusion to improve supersonic jet data",
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.",
author = "Ruscher, {Christopher J.} and Sivaram Gogineni and Magstadt, {Andrew S.} and Berry, {Matthew G.} and Glauser, {Mark N}",
year = "2018",
month = "1",
day = "1",
doi = "10.2514/6.2018-1735",
language = "English (US)",
isbn = "9781624105241",
booktitle = "AIAA Aerospace Sciences Meeting",
publisher = "American Institute of Aeronautics and Astronautics Inc, AIAA",
edition = "210059",

}

TY - GEN

T1 - Data fusion to improve supersonic jet data

AU - Ruscher, Christopher J.

AU - Gogineni, Sivaram

AU - Magstadt, Andrew S.

AU - Berry, Matthew G.

AU - Glauser, Mark N

PY - 2018/1/1

Y1 - 2018/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85044423672&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85044423672&partnerID=8YFLogxK

U2 - 10.2514/6.2018-1735

DO - 10.2514/6.2018-1735

M3 - Conference contribution

SN - 9781624105241

BT - AIAA Aerospace Sciences Meeting

PB - American Institute of Aeronautics and Astronautics Inc, AIAA

ER -