基于深度学习的高速直升机旋翼翼型气动优化设计

doi:10.7527/S1000-6893.2023.29828

Abstract

Abstract:

To optimize the aerodynamic shape of high-speed helicopter rotor airfoils， a multi-objective optimization framework is proposed based on deep learning. Firstly， a deep neural network is constructed as a surrogate model to predict the aerodynamic coefficients of rotor airfoils. The rotor airfoil SC1095 is selected as the baseline airfoil. The Class function/Shape function Transformation （CST） method is employed to parameterize the airfoil， and the Latin hypercube sampling method is used to generate the airfoil dataset for training deep neural networks. Then， comprehensively considering the aerodynamic performance of multiple design points such as forward flight， maneuvering and hover of the helicopter， a multi-objective aerodynamic shape optimization of the high-speed helicopter rotor airfoil is conducted by combining the deep neural network surrogate model with the multi-island genetic algorithm. The optimization results show that compared with the baseline airfoil， the optimized airfoil can significantly improve its forward flight performance without compromising hover and maneuvering performance. Finally， a rigid coaxial rotor is generated using the baseline and optimized airfoil respectively. The aerodynamic performance of these rotors in forward flight is computed and analyzed. The results indicate that the optimized airfoil significantly enhances the aerodynamic performance of the high-speed helicopter rotor.

Key words: rotor airfoil, aerodynamic shape optimization, deep learning, surrogate model, rigid coaxial rotor

CLC Number:

V221⁺.3

Jiaqi LIU, Rongqian CHEN, Jinhua LOU, Xu HAN, Hao WU, Yancheng YOU. Aerodynamic shape optimization of high-speed helicopter rotor airfoil based on deep learning[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(9): 529828-529828.

Figures/Tables 22

Fig.1

Fig.2

Table 1

Design points， objectives and constraints for a rotor airfoil

Design point	Parameter	Objective	Constraint
No.1	Ma = Ma_dd， C_L = 0， $α$ =-0.74°， Re=3.59×10⁶	min C_D₁	$C m 1 ≤ C m 1 0$
No.2	Ma = 0.4， C_L = C_l_max， $α$ = 10.5°， Re = 1.84×10⁶	max C_L₂
No.3	Ma = 0.6， C_L = 0.6， $α$ = 3.6°， Re = 2.76×10⁶	min C_D₃

Table 1

Fig.3

Fig.4

Table 2

Comparison of aerodynamic coefficients and experiment data of RAE2822 airfoil

Item	$C L$	$Δ C L$ /%	$C D$	$Δ C D$ /%
Experiment	0.803		0.016 8
Coarse grid	0.791	-1.49	0.019 2	14.29
Medium grid	0.793	-1.25	0.017 5	4.17
Fine grid	0.802	-0.12	0.017 1	1.79

Table 2

Fig.5

Fig.6

Fig.7

Table 3

Fig.8

Fig.9

Fig.10

Table 4

Fig.11

Fig.12

Fig.13

Fig.14

Fig.15

Fig.16

Fig.17

Fig.18

References 34

1	吴希明，牟晓伟. 直升机关键技术及未来发展与设想［J］. 空气动力学学报， 2021， 39（3）： 1-10.
	WU X M， MU X W. A perspective of the future development of key helicopter technologies［J］. Acta Aerodynamica Sinica， 2021， 39（3）： 1-10 （in Chinese）.
2	邓景辉. 直升机技术发展与展望［J］. 航空科学技术， 2021， 32（1）： 10-16.
	DENG J H. Development and prospect of helicopter technology［J］. Aeronautical Science & Technology， 2021， 32（1）： 10-16 （in Chinese）.
3	吴希明. 高速直升机发展现状、趋势与对策［J］. 南京航空航天大学学报， 2015， 47（2）： 173-179.
	WU X M. Current status， development trend and countermeasure for high-speed rotorcraft［J］. Journal of Nanjing University of Aeronautics & Astronautics， 2015， 47（2）： 173-179 （in Chinese）.
4	ALLEN L D， LIM J W， HAEHNEL R B， et al. Rotor blade design framework for airfoil shape optimization with performance considerations［C］∥AIAA Scitech 2021 Forum. Reston： AIAA， 2021： 0068.
5	韩忠华，高正红，宋文萍，等. 翼型研究的历史、现状与未来发展［J］. 空气动力学学报， 2021， 39（6）： 1-36.
	HAN Z H， GAO Z H， SONG W P， et al. On airfoil research and development： history， current status， and future directions［J］. Acta Aerodynamica Sinica， 2021， 39（6）： 1-36 （in Chinese）.
6	张卫国，孙俊峰，招启军，等. 旋翼翼型气动设计与验证方法［J］. 空气动力学学报， 2021， 39（6）： 136-148， 155.
	ZHANG W G， SUN J F， ZHAO Q J， et al. Aerodynamic design and verification methods of rotor airfoils［J］. Acta Aerodynamica Sinica， 2021， 39（6）： 136-148， 155 （in Chinese）.
7	李萍，庄开莲，李静. 国外直升机旋翼翼型研究综述［J］. 直升机技术， 2007（3）： 103-109.
	LI P， ZHUANG K L， LI J. Summary of research on helicopter rotor airfoil abroad［J］. Helicopter Technique， 2007（3）： 103-109 （in Chinese）.
8	丁存伟，杨旭东. 一种旋翼翼型多点多约束气动优化设计策略［J］. 航空计算技术， 2013， 43（1）： 52-57.
	DING C W， YANG X D. Multi-point aerodynamic optimization design strategy of rotor airfoil with multi-constrain conditions［J］. Aeronautical Computing Technique， 2013， 43（1）： 52-57 （in Chinese）.
9	JONES B， CROSSLEY W， LYRINTZIS A. Aerodynamic and aeroacoustic optimization of airfoils via a parallel genetic algorithm［C］∥ 7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization. Reston： AIAA， 1998： 4811.
10	王清，招启军. 基于遗传算法的旋翼翼型综合气动优化设计［J］. 航空动力学报， 2016， 31（6）： 1486-1495.
	WANG Q， ZHAO Q J. Synthetical optimization design of rotor airfoil by genetic algorithm［J］. Journal of Aerospace Power， 2016， 31（6）： 1486-1495 （in Chinese）.
11	宋超，周铸，李伟斌，等. 旋翼翼型高维多目标气动优化设计［J］. 北京航空航天大学学报， 2022， 48（1）： 95-105.
	SONG C， ZHOU Z， LI W B， et al. Many-objective aerodynamic optimization design for rotor airfoils［J］. Journal of Beijing University of Aeronautics and Astronautics， 2022， 48（1）： 95-105 （in Chinese）.
12	VU N A， LEE J W， SHU J I. Aerodynamic design optimization of helicopter rotor blades including airfoil shape for hover performance［J］. Chinese Journal of Aeronautics， 2013， 26（1）： 1-8.
13	杨慧，宋文萍，韩忠华，等. 旋翼翼型多目标多约束气动优化设计［J］. 航空学报， 2012， 33（7）： 1218-1226.
	YANG H， SONG W P， HAN Z H， et al. Multi-objective and multi-constrained optimization design for a helicopter rotor airfoil［J］. Acta Aeronautica et Astronautica Sinica， 2012， 33（7）： 1218-1226 （in Chinese）.
14	孙俊峰，卢风顺，黄勇，等. 旋翼翼型气动设计与评估软件HRADesign［J］. 空气动力学学报， 2021， 39（4）： 59-68.
	SUN J F， LU F S， HUANG Y， et al. Rotor airfoil aerodynamic design and evaluation software HRADesign［J］. Acta Aerodynamica Sinica， 2021， 39（4）： 59-68 （in Chinese）.
15	孙俊峰，刘刚，江雄，等. 基于Kriging模型的旋翼翼型优化设计研究［J］. 空气动力学学报， 2013， 31（4）： 437-441.
	SUN J F， LIU G， JIANG X， et al. Research of rotor airfoil design optimization based on the Kriging model［J］. Acta Aerodynamica Sinica， 2013， 31（4）： 437-441 （in Chinese）.
16	崔森润，李国强，张卫国，等. 直升机旋翼翼型高效优化设计方法［J］. 航空动力学报， 2023，doi： 10.13224/j.cnki.jasp.20220819 .
	CUI S R， LI G Q， ZHANG W G， et al. Efficient optimization design method of helicopter rotor airfoil［J］. Journal of Aerospace Power， 2023，doi： 10.13224/j.cnki.jasp.20220819 （in Chinese）.
17	ZHAO K， GAO Z H， HUANG J T， et al. Aerodynamic optimization of rotor airfoil based on multi-layer hierarchical constraint method［J］. Chinese Journal of Aeronautics， 2016， 29（6）： 1541-1552.
18	尚克明，招启军，王海. 基于Euler方程的直升机旋翼翼型反设计方法［J］. 直升机技术， 2008（3）： 92-97.
	SHANG K M， ZHAO Q J， WANG H. An inverse design method for the helicopter rotor airfoil based on Euler equation［J］. Helicopter Technique， 2008（3）： 92-97 （in Chinese）.
19	尚克明，招启军，赵国庆，等. 直升机旋翼翼型及桨叶气动外形反设计分析［J］. 南京航空航天大学学报， 2010， 42（5）： 550-556.
	SHANG K M， ZHAO Q J， ZHAO G Q， et al. Inverse design analysis on helicopter rotor airfoils and aerodynamic shapes［J］. Journal of Nanjing University of Aeronautics & Astronautics， 2010， 42（5）： 550-556 （in Chinese）.
20	赵国庆，招启军. 基于目标压力分布的旋翼先进气动外形反设计分析方法［J］. 航空学报， 2014， 35（3）： 744-755.
	ZHAO G Q， ZHAO Q J. Inverse design analysis method on rotor with advanced aerodynamic configuration based upon target pressure distribution［J］. Acta Aeronautica et Astronautica Sinica， 2014， 35（3）： 744-755 （in Chinese）.
21	赵欢，高正红，夏露. 基于新型多可信度代理模型的多目标优化方法［J］. 航空学报， 2023， 44（6）： 126962.
	ZHAO H， GAO Z H， XIA L. Novel multi-fidelity surrogate model assisted many-objective optimization method［J］. Acta Aeronautica et Astronautica Sinica， 2023， 44（6）： 126962 （in Chinese）.
22	陈笑天，吴裕平，田旭. 旋翼翼型中高速综合气动优化设计方法研究［J］. 航空科学技术， 2019， 30（9）： 19-24.
	CHEN X T， WU Y P， TIAN X. Research on comprehensive aerodynamic optimum design method of rotor airfoil at medium and high speed［J］. Aeronautical Science & Technology， 2019， 30（9）： 19-24 （in Chinese）.
23	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）.
24	BRUNTON S L， NOACK B R， KOUMOUTSAKOS P. Machine learning for fluid mechanics［J］. Annual Review of Fluid Mechanics， 2020， 52： 477-508.
25	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.
26	陈海昕，邓凯文，李润泽. 机器学习技术在气动优化中的应用［J］. 航空学报， 2019， 40（1）： 522480.
	CHEN H X， DENG K W， LI R Z. Utilization of machine learning technology in aerodynamic optimization［J］. Acta Aeronautica et Astronautica Sinica， 2019， 40（1）： 522480 （in Chinese）.
27	孙刚，王聪，王立悦，等. 人工智能在气动设计中的应用与展望［J］. 民用飞机设计与研究， 2021（3）： 1-9， 147.
	SUN G， WANG C， WANG L Y， et al. Application and prospect of artificial intelligence in aerodynamic design［J］. Civil Aircraft Design & Research， 2021（3）： 1-9， 147 （in Chinese）.
28	LI J C， DU X S， MARTINS J R R A. Machine learning in aerodynamic shape optimization［J］. Progress in Aerospace Sciences， 2022， 134： 100849.
29	KUTZ J N. Deep learning in fluid dynamics［J］. Journal of Fluid Mechanics， 2017， 814： 1-4.
30	Dadone L U. Dynamic and analytical study of a rotor airfoil： NASA CR-2988［R］. Washington， D. C.： NASA， 1987.
31	COOK P H， FIRMIN M C P， MCDONALD M A. Aerofoil RAE 2822： Pressure distributions， and boundary layer and wake measurements［R］. Pairs： AGARD， 1979.
32	KULFAN B M. Universal parametric geometry representation method［J］. Journal of Aircraft， 2008， 45（1）： 142-158.
33	BAGAI A. Aerodynamic Design of the Sikorsky X2 Technology Demonstrator™ Main Rotor Blade［C］∥American Helicopter Society 64th Annual Forum Proceedings. Rockville： American Helicopter Society， 2008： 1-16.
34	LIU J Q， CHEN R Q， SONG Q C， et al. Active flow control of helicopter rotor based on coflow jet［J］. International Journal of Aerospace Engineering， 2022， 2022： 9299470.

Layer number	Neuron number	Activation function
Hidden layer 1	512	ReLU
Hidden layer 2	256	ReLU
Hidden layer 3	128	ReLU
Hidden layer 4	64	ReLU
Output layer	3

Coefficient	Baseline	Optimized	Δ/%
C_D₁	0.010 6	0.009 7	-8.5
C_L₂	1.233 8	1.257 6	9.1
C_D₃	0.012 8	0.012 6	-1.6

[1]	Zhuangzhuang CUI, Xin YUAN, Guoqing ZHAO, Simeng JING, Qijun ZHAO. Influence of control strategy on forward flight performance of coaxial rigid rotor high⁃speed helicopters [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(9): 529256-529256.
[2]	Shusheng CHEN, Cong FENG, Zhaokang ZHANG, Ke ZHAO, Xinyang ZHANG, Zhenghong GAO. Aerodynamic design of wide-speed-range waverider-wing configuration based on global & gradient optimization method [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(6): 629596-629596.
[3]	Yuhan LI, Baoyu YANG, Yinong WU, Qiang ZHANG, Xiao TANG. Research on parameters correction method for thermal model of satellite optomechanical load [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(6): 628814-628814.
[4]	Xudong LUO, Yiquan WU, Jinlin CHEN. Research progress on deep learning methods for object detection and semantic segmentation in UAV aerial images [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(6): 28822-028822.
[5]	Haiqiao LIU, Meng LIU, Zichao GONG, Jing DONG. A review of image matching methods based on deep learning [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(3): 28796-028796.
[6]	Jing YU, Anlin JIANG, Liang LIU, Xiaojun WU, Yewei GUI, Shenshen LIU. PCA aerodynamic geometry parametrization method [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(10): 129125-129125.
[7]	Liqun CHEN, Xu ZOU, Lei ZHANG, Yingpan ZHU, Gang WANG, Jinyong CHEN. On⁃board intelligent target detection technology based on domestic commercial components [J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(S2): 728860-728860.
[8]	Xin SU, Runcheng GUAN, Qiao WANG, Weizheng YUAN, Xianglian LYU, Yang HE. Ice area and thickness detection method based on deep learning [J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(S2): 729283-729283.
[9]	Pengyu LIU, Xueyao ZHU. Semantic parsing technology of air traffic control instruction in fusion airspace based on deep learning [J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(S1): 727592-727592.
[10]	Huan ZHAO, Zhenghong GAO, Lu XIA. Novel multi-fidelity surrogate model assisted many-objective optimization method [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(6): 126962-126962.
[11]	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.
[12]	Huan ZHAO, Zhenghong GAO, Lu XIA. Aerodynamic shape design optimization method based on novel high⁃dimensional surrogate model [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(5): 126924-126924.
[13]	Zhiyang ZHENG, Yang ZHANG, Zhao ZHANG, Baohai WU, Ying ZHANG. Layout optimization of auxiliary support for thin-walled blade based on GA-SVR [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(4): 426805-426805.
[14]	Ting YU, Luyi LI, Yushan LIU, Zeming CHANG. Efficient Bayesian updating method under observation uncertainty and its application in wing structure [J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(24): 228592-228592.
[15]	Peng DING, Yafei SONG. A cost-sensitive method for aerial target intention recognition [J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(24): 328551-328551.

Aerodynamic shape optimization of high-speed helicopter rotor airfoil based on deep learning

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 22

References 34

Related Articles 15

Recommended Articles

Metrics

Comments