基于卷积神经网络的结冰翼型气动特性建模

doi:10.7527/S10006893.2021.26434

Abstract

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

CLC Number:

V211.3

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.

Figures/Tables 22

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功能层	net0	net1	net2	net3
Input	200×200	200×200	200×200	200×200
Conv1	32，3×3；2×2	32，3×3；2×2	32，3×3；2×2	32，3×3；2×2
Conv2		64， 3×3；2×2	64， 3×3；2×2	64， 3×3；2×2
Conv3			128， 3×3；2×2	128， 3×3；2×2
Conv4				128， 3×3；2×2
Output	21	21	21	21

系数	指标		net0	net1	net2	net3
C_L	ε_avg/%	训练	5.736	4.440	3.709	3.739
	ε_avg/%	测试	8.971	8.041	8.013	7.983
	训练耗时/s		3 970	9 454	20 753	33 939
C_D	ε_avg/%	训练	7.730	6.758	5.503	5.858
	ε_avg/%	测试	9.787	8.397	8.256	8.245
	训练耗时/s		3 947	10 743	20 616	34 308

功能层	net-fn8	net-fn16	net-fn32	net-fn64
Input	200×200	200×200	200×200	200×200
Conv1	8， 3×3；2×2	16， 3×3；2×2	32， 3×3；2×2	64， 3×3；2×2
Conv2	16， 3×3；2×2	32， 3×3；2×2	64， 3×3；2×2	128， 3×3；2×2
Output	21	21	21	21

系数	指标		net-fn8	net-fn16	net-fn32	net-fn64
C_L	ε_avg/%	训练	6.219	4.606	4.440	4.640
	ε_avg/%	测试	8.551	8.148	8.041	8.683
	训练耗时/s		2 720	4 830	9 454	20 291
C_D	ε_avg/%	训练	14.354	24.795	6.758	7.352
	ε_avg/%	测试	14.697	25.050	8.397	8.722
	训练耗时/s		2 995	5 419	10 743	22 411

功能层	net-fs3	net-fs5	net-fs7
Input	200×200	200×200	200×200
Conv1	32， 3×3；2×2	32， 5×5；2×2	32， 7×7；2×2
Conv2	64， 3×3；2×2	64， 5×5；2×2	64， 7×7；2×2
Conv3	128， 3×3；2×2	128， 5×5；2×2	128， 7×7；2×2
Output	21	21	21
Layer	200×200	200×200	200×200

Aerodynamic characteristics modeling of iced airfoil based on convolution neural networks

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