Aerodynamic characteristics modeling of iced airfoil based on convolution neural networks

Lei HE; Weiqi QIAN; Kangsheng DONG; Xian YI; Congcong CHAI

doi:10.7527/S10006893.2021.26434

ACTA AERONAUTICAET ASTRONAUTICA SINICA >

2023 , Vol. 44 >Issue 5: 126434 - 126434

DOI: https://doi.org/10.7527/S10006893.2021.26434

Fluid Mechanics and Flight Mechanics

Aerodynamic characteristics modeling of iced airfoil based on convolution neural networks

Lei HE ,
Weiqi QIAN ,
Kangsheng DONG ,
Xian YI ,
Congcong CHAI

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^1.State Key Laboratory of Aerodynamics，China Aerodynamics Research and Development Center，Mianyang 　621000，China
^2.Computational Aerodynamics Research Institute，China Aerodynamics Research and Development Center，Mianyang 　621000，China

E-mail： yixian_2000@163.com

Received date: 2021-09-24

Revised date: 2021-10-15

Accepted date: 2021-11-08

Online published: 2021-11-12

Supported by

National Level Project

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Abstract

A prediction method for iced airfoil aerodynamic characteristics is proposed based on the Convolutional Neural Networks （CNN）. This new method is able to solve the multiple-value problem of the complex ice shape along the wall-normal direction at a specific location of the airfoils. The CNN prediction model can directly predict the lift and drag coefficient at multiple angles of attacks via a rapid process from the iced airfoil image to aerodynamics characteristics. The relative mean errors of the resulting lift coefficient and drag coefficient are smaller than 8%. By comparison， the influence of the convolutional layer number， filter number， and filter size on the model performance is investigated. The features of different layers in the CNN correspond to different filter frequencies， and increasing the number of convolutional layers will capture more features of high frequency. With the increase in the number of filters， more features of the ice shape will be extracted， promoting the model performance. However， more redundant features will appear if the number of filters exceeds a critical value， resulting in degradation of the generalization performance. The required number of filters for drag coefficient prediction is larger than that for lift coefficient， because drag coefficient is affected by both the pressure difference on the airfoil and friction， leading to a larger number of key features required for drag coefficient prediction than that for lift coefficient. Moreover， a larger size of filters will widen the field of view of convolution operation， enhancing the capability of extracting global features， and promoting the generalization performance of the model. The current new method can be applied to real-time prediction and monitor of aerodynamic characteristics of aircraft icing.

Key words： aircraft icing; aerodynamic characteristics; machine learning; deep learning; convolutional neural networks

Cite this article

Lei HE , Weiqi QIAN , Kangsheng DONG , Xian YI , Congcong CHAI . Aerodynamic characteristics modeling of iced airfoil based on convolution neural networks[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023 , 44(5) : 126434 -126434 . DOI: 10.7527/S10006893.2021.26434

References

1	郭向东，柳庆林，刘森云，等. 结冰风洞中过冷大水滴云雾演化特性数值研究［J］. 航空学报， 2020， 41（8）： 123655.
	GUO X D， LIU Q L， LIU S Y， et al. Numerical study of supercooled large droplet cloud evolution characteristics in icing wind tunnel［J］. Acta Aeronautica et Astronautica Sinica， 2020， 41（8）： 123655 （in Chinese）.
2	易贤，王斌，李伟斌，等. 飞机结冰冰形测量方法研究进展［J］. 航空学报， 2017， 38（2）： 520711.
	YI X， WANG B， LI W B， et al. Research progress on ice shape measurement approaches for aircraft icing［J］. Acta Aeronautica et Astronautica Sinica， 2017， 38（2）： 520711 （in Chinese）.
3	桂业伟，周志宏，李颖晖，等. 关于飞机结冰的多重安全边界问题［J］. 航空学报， 2017， 38（2）： 520734.
	GUI Y W， ZHOU Z H， LI Y H， et al. Multiple safety boundaries protection on aircraft icing［J］. Acta Aeronautica et Astronautica Sinica， 2017， 38（2）： 520734 （in Chinese）.
4	KIM H， BRAGG M. Effects of leading-edge ice accretion geometry on airfoil performance［C］∥ 17th Applied Aerodynamics Conference. Reston： AIAA， 1999.
5	BRAGG M， HUTCHISON T， MERRET J. Effect of ice accretion on aircraft flight dynamics［C］∥ 38th Aerospace Sciences Meeting and Exhibit. Reston： AIAA， 2000.
6	钟长生，王立新. 结冰对飞机动力学特性影响的分析方法及其进展［J］. 飞行力学， 2004， 22（4）： 22-24， 84.
	ZHONG C S， WANG L X. Analysis methods and its development about effect of ice accretion on aircraft flight dynamics characteristics［J］. Flight Dynamics， 2004， 22（4）： 22-24， 84 （in Chinese）.
7	POKHARIYAL D， BRAGG M， HUTCHISON T， et al. Aircraft flight dynamics with simulated ice accretion［C］∥ 39th Aerospace Sciences Meeting and Exhibit. Reston： AIAA， 2001.
8	袁坤刚，曹义华. 积冰几何特性对翼型性能影响的神经网络预测［J］. 北京航空航天大学学报， 2008， 34（8）： 900-903.
	YUAN K G， CAO Y H. Effect of ice geometry to airfoil performance using neural networks prediction［J］. Journal of Beijing University of Aeronautics and Astronautics， 2008， 34（8）： 900-903 （in Chinese）.
9	HINTON G E， SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks［J］. Science， 2006， 313（5786）： 504-507.
10	吴正文. 卷积神经网络在图像分类中的应用研究［D］. 成都：电子科技大学， 2015.
	WU Z W. Application research of convolution neural network in image classification［D］. Chengdu： University of Electronic Science and Technology of China， 2015 （in Chinese）.
11	RADFORD A， METZ L， CHINTALA S. Unsupervised representation learning with deep convolutional generative adversarial networks［EB/OL］. 2015： arXiv： 1511.06434. .
12	GATYS L A， ECKER A S， BETHGE M. A neural algorithm of artistic style［EB/OL］. 2015： arXiv： 1508.06576. .
13	KARPATHY A， TODERICI G， SHETTY S， et al. Large-scale video classification with convolutional neural networks［C］∥2014 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE Press， 2014： 1725-1732.
14	SEIDE F， LI G， YU D. Conversational speech transcription using context-dependent deep neural networks［C］∥ Interspeech 2011. ISCA： ISCA， 2011： 437-440.
15	王怡星，韩仁坤，刘子扬，等. 流体力学深度学习建模技术研究进展［J］. 航空学报， 2021， 42（4）： 524779.
	WANG Y X， HAN R K， LIU Z Y， et al. Progress of deep learning modeling technology for fluid mechanics［J］. Acta Aeronautica et Astronautica Sinica， 2021， 42（4）： 524779 （in Chinese）.
16	张伟伟，寇家庆，刘溢浪. 智能赋能流体力学展望［J］. 航空学报， 2021， 42（4）： 524689.
	ZHANG W W， KOU J Q， LIU Y L. Prospect of artificial intelligence empowered fluid mechanics［J］. Acta Aeronautica et Astronautica Sinica， 2021， 42（4）： 524689 （in Chinese）.
17	KUTZ J N. Deep learning in fluid dynamics［J］. Journal of Fluid Mechanics， 2017， 814： 1-4.
18	YILMAZ E， GERMAN B. A convolutional neural network approach to training predictors for airfoil performance［C］∥ 18th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston： AIAA， 2017.
19	ZHANG Y， SUNG W J， MAVRIS D. Application of convolutional neural network to predict airfoil lift coefficient［DB/OL］. arXiv preprint： 1712.10082，2017.
20	WU J L， WANG J X， XIAO H， et al. Physics-informed machine learning for predictive turbulence modeling： a priori assessment of prediction confidence［DB/OL］. arXiv preprint： 1607.04563， 2016.
21	HUANG J J， DUAN L， WANG J X， et al. High-mach-number turbulence modeling using machine learning and direct numerical simulation database［C］∥ 55th AIAA Aerospace Sciences Meeting. Reston： AIAA， 2017.
22	LING J L， KURZAWSKI A， TEMPLETON J. Reynolds averaged turbulence modelling using deep neural networks with embedded invariance［J］. Journal of Fluid Mechanics， 2016， 807： 155-166.
23	张伟伟，朱林阳，刘溢浪，等. 机器学习在湍流模型构建中的应用进展［J］. 空气动力学学报， 2019， 37（3）： 444-454.
	ZHANG W W， ZHU L Y， LIU Y L， et al. Progresses in the application of machine learning in turbulence modeling［J］. Acta Aerodynamica Sinica， 2019， 37（3）： 444-454 （in Chinese）.
24	廖鹏，姚磊江，白国栋，等. 基于深度学习的混合翼型前缘压力分布预测［J］. 航空动力学报， 2019， 34（8）： 1751-1758.
	LIAO P， YAO L J， BAI G D， et al. Prediction of hybrid airfoil leading edge pressure distribution based on deep learning［J］. Journal of Aerospace Power， 2019， 34（8）： 1751-1758 （in Chinese）.
25	RAISSI M， YAZDANI A， KARNIADAKIS G E. Hidden fluid mechanics： Learning velocity and pressure fields from flow visualizations［J］. Science， 2020， 367（6481）： 1026-1030.
26	GUO X X， LI W， IORIO F. Convolutional neural networks for steady flow approximation［C］∥ Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York： ACM， 2016： 481-490.
27	LEE S， YOU D. Data-driven prediction of unsteady flow over a circular cylinder using deep learning［J］. Journal of Fluid Mechanics， 2019， 879： 217-254.
28	叶舒然，张珍，王一伟，等. 基于卷积神经网络的深度学习流场特征识别及应用进展［J］. 航空学报， 2021， 42（4）： 524736.
	YE S R， ZHANG Z， WANG Y W， et al. Progress in deep convolutional neural network based flow field recognition and its applications［J］. Acta Aeronautica et Astronautica Sinica， 2021， 42（4）： 524736 （in Chinese）.
29	LECUN Y， BOTTOU L， BENGIO Y， et al. Gradient-based learning applied to document recognition［J］. Proceedings of the IEEE， 1998， 86（11）： 2278-2324.
30	LECUN Y， BENGIO Y， HINTON G. Deep learning［J］. Nature， 2015， 521（7553）： 436-444.
31	陈海，钱炜祺，何磊. 基于深度学习的翼型气动系数预测［J］. 空气动力学学报， 2018， 36（2）： 294-299.
	CHEN H， QIAN W Q， HE L. Aerodynamic coefficient prediction of airfoils based on deep learning［J］. Acta Aerodynamica Sinica， 2018， 36（2）： 294-299 （in Chinese）.
32	GLOROT X， BENGIO Y. Understanding the difficulty of training deep feedforward neural networks［C］∥ International Conference on Artificial Intelligence and Statistics， 2010： 249-256.
33	NAIR V， HINTON G E. Rectified linear units improve restricted Boltzmann machines［C］∥ Proceedings of the 27th International Conference on Machine Learning， 2010： 807-814.
34	KRIZHEVSKY A， SUTSKEVER I， HINTON G E. Imagenet classification with deep convolutional neural networks［C］∥ The Advances in Neural Information Processing Systems， 2012： 1097-1105.
35	何磊，钱炜祺，汪清，等. 机器学习方法在气动特性建模中的应用［J］. 空气动力学学报， 2019， 37（3）： 470-479.
	HE L， QIAN W Q， WANG Q， et al. Applications of machine learning for aerodynamic characteristics modeling［J］. Acta Aerodynamica Sinica， 2019， 37（3）： 470-479 （in Chinese）.
36	CHEN H， HE L， QIAN W Q， et al. Multiple aerodynamic coefficient prediction of airfoils using a convolutional neural network［J］. Symmetry， 2020， 12（4）： 544.
37	何磊，钱炜祺，易贤，等. 基于转置卷积神经网络的翼型结冰冰形图像化预测方法［J］. 国防科技大学学报， 2021， 43（3）： 98-106.
	HE L， QIAN W Q， YI X， et al. Graphical prediction method of airfoil ice shape based on transposed convolution neural networks［J］. Journal of National University of Defense Technology， 2021， 43（3）： 98-106 （in Chinese）.
38	BRAGG M B. An experimental study of the aerodynamics of a NACA 0012 airfoil with a simulated glaze ice accretion： NASA-CR-179897［R］. Washington， D.C.： NASA， 1986.
39	BRAGG M B， et al. Iced-airfoil aerodynamics［J］. Progress in Aerospace Sciences， 2005， 41（5）： 323-362.
40	BRAGG M B， KHODADOUST A， SPRING S A. Measurements in a leading-edge separation bubble due to a simulated airfoil ice accretion［J］. AIAA Journal， 1992， 30（6）： 1462-1467.
41	CARL O G. GRUMMP version 0.2.1 user's guide［R］. Columbia： University of British Columbia， 1997.
42	张耀冰，邓有奇，吴晓军，等. DLR-F6翼身组合体数值计算［J］. 空气动力学学报， 2011， 29（2）： 163-169.
	ZHANG Y B， DENG Y Q， WU X J， et al. Drag prediction of DLR-F6 using MFlow unstructured mesh solver［J］. Acta Aerodynamica Sinica， 2011， 29（2）： 163-169 （in Chinese）.
43	KINGMA D P， BA J. Adam： A method for stochastic optimization［DB/OL］.arXiv preprint： 1412.6980， 2014.

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