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Multi-Fidelity Aerodynamic Dataset Generation of a Fighter Aircraft with a Deep Neural-Genetic Network
Date
2021-01-01
Author
Millidere, Murat
Gomec, Fazil Selcuk
Kurt, Huseyin Burak
Akgül, Ferhat
Metadata
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© 2021, American Institute of Aeronautics and Astronautics Inc.. All rights reserved.This paper is a follow-up study on prior research work on multi-fidelity aerodynamic dataset generation. The prior work studied a comparison of modified Variable-Complexity Modelling and co-Kriging methods applied to F-16 fighter aircraft. In this research, the multi-fidelity deep neural-genetic network method is introduced. The results provide evidence that the deep neural-genetic network method in this paper can be employed in dealing with the aerodynamic data fusion problem.
URI
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85126821331&origin=inward
https://hdl.handle.net/11511/98866
DOI
https://doi.org/10.2514/6.2021-3007
Conference Name
AIAA Aviation and Aeronautics Forum and Exposition, AIAA AVIATION Forum 2021
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Department of Engineering Sciences, Conference / Seminar
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M. Millidere, F. S. Gomec, H. B. Kurt, and F. Akgül, “Multi-Fidelity Aerodynamic Dataset Generation of a Fighter Aircraft with a Deep Neural-Genetic Network,” presented at the AIAA Aviation and Aeronautics Forum and Exposition, AIAA AVIATION Forum 2021, Virtual, Online, 2021, Accessed: 00, 2022. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85126821331&origin=inward.
