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Vol. 32, No. 8(2), S&M2292

ISSN (print) 0914-4935
ISSN (online) 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials, Volume 32, Number 10(1) (2020)
Copyright(C) MYU K.K.
pp. 3169-3184
S&M2331 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2020.2845
Published: October 9, 2020

Aircraft Shape Design Using Artificial Neural Network [PDF]

Der-Chen Huang, Yu-Fu Lin, Lee-Jang Yang, and Wei-Ming Chen

(Received February 28, 2020; Accepted June 30, 2020)

Keywords: aerodynamic coefficient, computational fluid dynamics, wind tunnel experiments, artificial neural network

To date, the aerodynamic coefficient of an aircraft has been obtained by computational fluid dynamics (CFD) or wind tunnel experiments, which have a high cost. To reduce the cost and period of analysis, we adopt big data analysis and AI techniques to build an artificial neural network (ANN) and perform learning and training based on historical flight and wind tunnel experiment parameters, so as to predict the aerodynamic coefficient of aircraft. Experimental results show that the values obtained by the proposed method are close to those obtained by wind tunnel experiments. Consequently, the proposed method can effectively reduce the amount of simulation analysis by CFD and wind tunnel experiments.

Corresponding author: Wei-Ming Chen


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Cite this article
Der-Chen Huang, Yu-Fu Lin, Lee-Jang Yang, and Wei-Ming Chen, Aircraft Shape Design Using Artificial Neural Network, Sens. Mater., Vol. 32, No. 10, 2020, p. 3169-3184.



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