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.
N1 - Funding Information:
The authors would like to acknowledge the following organizations and individuals: our funding source, a SBIR Phase II with AFRL turbine engine branch under the direction of Thomas Ferrill; Dr. Barry Kiel and Dr. Alex Giese for providing technical expertise; Dr. Kamal Viswanath and Dr. Kailas Kailasanath of the Naval research laboratory for letting us use their JERNE code and for all of their advice on the computational work presented in this paper; Matthew Berry, and Thomas Coleman from Syracuse University for providing key experimental data and insight; Jacques Lewalle, Pinqing Kan, and Andrew Tenney for all of their assistance in analyzing the computational data and obtaining very useful results. The simulation data provided in this paper were generated using Thunder and Topaz two systems on the DoD’s HPCMP. This paper was assigned a clearance of CLEARED on 17 November 2017 by the 88th Air Base Wing. The reference number for this paper is 88ABW-2017-5816.
Publisher Copyright:
© 2018 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.
PY - 2018
Y1 - 2018
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.
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U2 - 10.2514/6.2018-1735
DO - 10.2514/6.2018-1735
M3 - Conference contribution
AN - SCOPUS:85141568812
SN - 9781624105241
T3 - AIAA Aerospace Sciences Meeting, 2018
BT - AIAA Aerospace Sciences Meeting
PB - American Institute of Aeronautics and Astronautics Inc, AIAA
T2 - AIAA Aerospace Sciences Meeting, 2018
Y2 - 8 January 2018 through 12 January 2018
ER -