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Online: ISSN 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.
Online: ISSN 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
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
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Sensors and Materials
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is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
Instructions to authors
English 日本語
Instructions for manuscript preparation
English 日本語
Template
English
Publisher
MYU K.K.
Sensors and Materials
1-23-3-303 Sendagi,
Bunkyo-ku, Tokyo 113-0022, Japan
Tel: 81-3-3827-8549
Fax: 81-3-3827-8547
MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.
MYU Research
(proofreading and recording)
MYU K.K.
(translation service)
The Art of Writing Scientific Papers
(How to write scientific papers)
(Japanese Only)
Sensors and Materials, Volume 32, Number 10(1) (2020)
Copyright(C) MYU K.K.
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  This work is licensed under a Creative Commons Attribution 4.0 International License. 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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