基于随机森林的飞行载荷代理模型分析方法

doi:10.7527/S1000-6893.2021.25640

Abstract

Abstract: Flight Load Computation (FLC) consumes considerable computational time and resources, and promoting FLC efficiency is highly significant to shorten the development cycle and improve the design quality of aircraft. This paper focuses on a data-driven machine learning surrogate model for load analysis based on the Random Forest (RF), which fits the load analysis with important potential and application prospect in this field because of its advantages such as high learning efficiency, strong generalization ability, overfitting avoidance, parameter interpretability, and variable sensitivity analysis. The training data for the RF surrogate model using conventional load analysis methods and the SQL144 framework of NASTRAN are generated, and parameters such as height, Mach number, overload, and pitch angular acceleration serve as the input variables to establish the surrogate model of load prediction in the case of symmetric maneuver. The proposed model is used to predict other conditions and the shear force, bending moment, and torque of roots of wings and horizontal tail. The predicting results are evaluated and verified, demonstrating high accuracy of the RF surrogate model and its ability to drastically promote the FLC efficiency and conduct sensitivity analysis of the flight load to state parameters. This study provides a new approach to the efficient and comprehensive flight load analysis.

Key words: flight load, surrogate model, machine learning, random forest, symmetric maneuver

CLC Number:

V215.1

LI Haiquan, CHEN Xiaoqian, ZUO Linxuan, ZHAO Zhuolin. Surrogate model for flight load analysis based on random forest[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(3): 225640-225640.

References

[1] MOHAMED K S. Machine learning for model order reduction[M]. Berlin:Springer International Publishing, 2018.
[2] GOODFELLOW I, BENGIO Y, COURVILLE A. Deep learning[M]. Cambridge:MIT Press, 2016:3-6.
[3] HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2016:770-778.
[4] KUTZ J N. Deep learning in fluid dynamics[J]. Journal of Fluid Mechanics, 2017, 814:1-4.
[5] 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.
[6] 张天姣, 钱炜祺, 周宇, 等. 人工智能与空气动力学结合的初步思考[J]. 航空工程进展, 2019, 10(1):1-11. ZHANG T J, QIAN W Q, ZHOU Y, et al. Preliminary thoughts on the combination of artificial intelligence and aerodynamics[J]. Advances in Aeronautical Science and Engineering, 2019, 10(1):1-11(in Chinese).
[7] 王博斌, 张伟伟, 叶正寅. 基于神经网络模型的动态非线性气动力辨识方法[J]. 航空学报, 2010, 31(7):1379-1388. WANG B B, ZHANG W W, YE Z Y. Unsteady nonlinear aerodynamics identification based on neural network model[J]. Acta Aeronautica et Astronautica Sinica, 2010, 31(7):1379-1388(in Chinese).
[8] 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 neuralnetwork based flow field recognition and its applications[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(4):524736(in Chinese).
[9] SUN Z W, WANG C, ZHENG Y, et al. Non-intrusive reduced-order model for predicting transonic flow with varying geometries[J]. Chinese Journal of Aeronautics, 2020, 33(2):508-519.
[10] GOU Y Y, LI H B, DONG X M, et al. Constrained adaptive neural network control of an MIMO aeroelastic system with input nonlinearities[J]. Chinese Journal of Aeronautics, 2017, 30(2):796-806.
[11] SECCO N R, DE MATTOS B S. Artificial neural networks to predict aerodynamic coefficients of transport airplanes[J]. Aircraft Engineering and Aerospace Technology, 2017, 89(2):211-230.
[12] 史志伟, 明晓. 基于模糊聚类的模糊神经网络在非定常气动力建模中的应用[J]. 空气动力学学报, 2005, 23(1):21-24. SHI Z W, MING X. The application of FNN in unsteady aerodynamics modeling based on fuzzy clustering[J]. Acta Aerodynamica Sinica, 2005, 23(1):21-24(in Chinese).
[13] 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).
[14] WANG Q, QIAN W Q, HE K F. Unsteady aerodynamic modeling at high angles of attack using support vector machines[J]. Chinese Journal of Aeronautics, 2015, 28(3):659-668.
[15] IGNATYEV D, KHRABROV A. Experimental study and neural network modeling of aerodynamic characteristics of canard aircraft at high angles of attack[J]. Aerospace, 2018, 5(1):26.
[16] IGNATYEV D I, KHRABROV A N. Neural network modeling of unsteady aerodynamic characteristics at high angles of attack[J]. Aerospace Science and Technology, 2015, 41:106-115.
[17] 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.
[18] HOLMES G, SARTOR P, REED S, et al. Prediction of landing gear loads using machine learning techniques[J]. Structural Health Monitoring, 2016, 15(5):568-582.
[19] JEONG S H, LEE K B, HAM J H, et al. Estimation of maximum strains and loads in aircraft landing using artificial neural network[J]. International Journal of Aeronautical and Space Sciences, 2020, 21(1):117-132.
[20] HALLE M, THIELECKE F. Local Model Networks applied to Flight Loads Estimation[C]//31st Congress of the International Council of the Aeronautical Sciences (ICAS), 2018.
[21] ALLEN M J, DIBLEY R P. Modeling aircraft wing loads from flight data using neural networks[C]//SAE Technical Paper Series. 400 Commonwealth Drive. Warrendale:SAE International, 2003.
[22] COOPER S B, DIMAIO D. Static load estimation using artificial neural network:Application on a wing rib[J]. Advances in Engineering Software, 2018, 125:113-125.
[23] 赵永辉. 气动弹性力学与控制[M]. 北京:科学出版社, 2007:267-283. ZHAO Y H. Aeroelasticity and control[M]. Beijing:Science Press, 2007:267-283(in Chinese).
[24] BREIMAN L. Random forests[J]. Machine Learning, 2001, 45:5-32.

Surrogate model for flight load analysis based on random forest

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	Qian YANG, Xiaofeng GUO, Qin LI, Wei DONG. Hot air anti-icing performance estimation method based on POD and surrogate model [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(1): 626992-626992.
[2]	Junjie NIU, Weimin SANG, Dong LI, Lian HAO, Zelin WANG. Icing test flight area determination method based on surrogated model of water droplet collection [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(1): 627697-627697.
[3]	Qilei GUO, Weimin SANG, Junjie NIU, Ye YUAN. UAV flight strategy considering icing risk under complex meteorological conditions [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(1): 627518-627518.
[4]	DUAN Yucong, WANG Xuede, ZHOU Xin, ZHANG Peiyu, GUO Xiyang, CHENG Xing, FAN Junwei. Machine learning of emission intensity signal of laser powder bed fusion molten pool [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(9): 425855-425855.
[5]	WU Zixuan, ZHANG Ning, GAO Kaiye, PENG Rui. Flight trip fuel volume prediction based on random forest with adjustment to risk preference [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(2): 224933-224933.
[6]	YE Nianhui, LONG Teng, WU Yufei, TANG Yifan, SHI Renhe. Kriging-assisted constrained differential evolution algorithm [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(6): 324580-324580.
[7]	WANG Zelin, JI Ritian, HUI Xinyu, DING Chen, WANG Hui, BAI Junqiang. L-shaped directional heat transfer based on deep learning [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(6): 124242-124242.
[8]	HU Weijie, HUANG Zenghui, LIU Xuejun, LYU Hongqiang. Missile aerodynamic performance prediction of Gaussian process through automatic kernel construction [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(4): 524093-524093.
[9]	FAN Zhouwei, YU Xiongqing, WANG Chao, ZHONG Bowen. Sensitivity analysis of key design parameters of commercial aircraft using deep neural network [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(4): 524353-524353.
[10]	LI Ni, BU Shuhui, SHANG Bolin, LI Yongbo, TANG Zhili, ZHANG Weiwei. Aircraft intelligent design: Visions and key technologies [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(4): 524752-524752.
[11]	FENG Yunwen, PAN Weihuang, LIU Jiaqi, LU Cheng, XUE Xiaofeng, LENG Jiaxing. Operational reliability of aircraft power plant based on machine learning [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(4): 524732-524732.
[12]	REN Feng, GAO Chuanqiang, TANG Hui. Machine learning for flow control: Applications and development trends [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(4): 524686-524686.
[13]	HE Chuangxin, DENG Zhiwen, LIU Yingzheng. Turbulent flow data assimilation and its applications [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(4): 524704-524704.
[14]	YU Min, LUO Jianjun, WANG Mingming. Real-time motion prediction of space tumbling targets based on machine learning [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(2): 324149-324149.
[15]	JIA Beixi, LYU Zhenzhou, LEI Jingyu. Parameterized simulation platform of turbine cooling film blade life reliability analysis [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(12): 224747-224747.