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.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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Aircraft Shape Design Using Artificial Neural Network

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 the 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 parameters 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 in wind tunnel experiments. Consequently, the proposed method can effectively reduce the amount of simulation analysis by CFD and the need for wind tunnel experiments.

Corresponding author: Wei-Ming Chen




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