Aircraft Shape Design Using Artificial Neural Network

被引：1

作者：

Huang, Der-Chen ^{[1
]}

Lin, Yu-Fu ^{[1
]}

Yang, Lee-Jang ^{[2
]}

Chen, Wei-Ming ^{[3
]}

机构：

[1] Natl Chung Hsing Univ, Dept Comp Sci & Engn, Taichung 402, Taiwan

[2] Natl Chung Shan Inst Sci & Technol, Aeronaut Syst Res Div, Taichung 407, Taiwan

[3] Natl Dong Hwa Univ, Dept Informat Management, Hualien 974, Taiwan

来源：

SENSORS AND MATERIALS | 2020年 / 32卷 / 10期

关键词：

aerodynamic coefficient; computational fluid dynamics; wind tunnel experiments; artificial neural network;

D O I：

10.18494/SAM.2020.2845

中图分类号：

TH7 [仪器、仪表];

学科分类号：

0804 ; 080401 ; 081102 ;

摘要：

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.

引用

页码：3169 / 3184

页数：16

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